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Adversarial domain adaptation with Siamese network for video object cosegmentation Signal Process. Image Commun. (IF 3.5) Pub Date : 2024-02-15 Li Xu, Yaodong Zhou, Bing Luo, Bo Li, Chao Zhang
Object cosegmentation aims to obtain common objects from multiple images or videos, which performs by employing handcraft features to evaluate region similarity or learning higher semantic information via deep learning. However, the former based on handcraft features is sensitive to illumination, appearance changes and clutter background to the domain gap. The latter based on deep learning needs the
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Prediction-based coding with rate control for lossless region of interest in pathology imaging Signal Process. Image Commun. (IF 3.5) Pub Date : 2024-01-22 Joan Bartrina-Rapesta, Miguel Hernández-Cabronero, Victor Sanchez, Joan Serra-Sagristà, Pouya Jamshidi, J. Castellani
Online collaborative tools for medical diagnosis produced from digital pathology images have experimented an increase in demand in recent years. Due to the large sizes of pathology images, rate control (RC) techniques that allow an accurate control of compressed file sizes are critical to meet existing bandwidth restrictions while maximizing retrieved image quality. Recently, some RC contributions
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A Dilated MultiRes Visual Attention U-Net for historical document image binarization Signal Process. Image Commun. (IF 3.5) Pub Date : 2024-01-15 Nikolaos Detsikas, Nikolaos Mitianoudis, Nikolaos Papamarkos
The task of binarization of historical document images has been in the forefront of image processing research, during the digital transition of libraries. The process of storing and transcribing valuable historical printed or handwritten material can salvage world cultural heritage and make it available online without physical attendance. The task of binarization can be viewed as a pre-processing step
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FAVER: Blind quality prediction of variable frame rate videos Signal Process. Image Commun. (IF 3.5) Pub Date : 2024-01-08 Qi Zheng, Zhengzhong Tu, Pavan C. Madhusudana, Xiaoyang Zeng, Alan C. Bovik, Yibo Fan
Video quality assessment (VQA) remains an important and challenging problem that affects many applications at the widest scales. Recent advances in mobile devices and cloud computing techniques have made it possible to capture, process, and share high resolution, high frame rate (HFR) videos across the Internet nearly instantaneously. Being able to monitor and control the quality of these streamed
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Concept drift challenge in multimedia anomaly detection: A case study with facial datasets Signal Process. Image Commun. (IF 3.5) Pub Date : 2024-01-08 Pratibha Kumari, Priyankar Choudhary, Vinit Kujur, Pradeep K. Atrey, Mukesh Saini
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Stereo vision based systems for sea-state measurement and floating structures monitoring Signal Process. Image Commun. (IF 3.5) Pub Date : 2024-01-06 Omar Sallam, Rihui Feng, Jack Stason, Xinguo Wang, Mirjam Fürth
Using computer vision techniques such as stereo vision systems for sea state measurement or for offshore structures monitoring can improve the measurement fidelity and accuracy with no significant additional cost. In this paper, two experiments (in-lab/open-sea) are conducted to study the performance of stereo vision system to measure the water wave surface elevation and rigid body heaving motion.
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Enhancing fine-detail image synthesis from text descriptions by text aggregation and connection fusion module Signal Process. Image Commun. (IF 3.5) Pub Date : 2024-01-02 Huaping Zhou, Tao Wu, Senmao Ye, Xinru Qin, Kelei Sun
Synthesizing images with fine details from text descriptions is a challenge. The existing single-stage generative adversarial networks (GANs) fuse sentence features into the image generation process through affine transformation, which alleviate the problems of missing details and large computation from stacked networks. However, existing single-stage networks ignore the word features in the text description
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Analyzing the effect of shot noise in indirect Time-of-Flight cameras Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-12-29 Nofre Sanmartin-Vich, Javier Calpe, Filiberto Pla
Continuous wave indirect Time-of-Flight cameras obtain depth images by emitting a modulated continuous light wave and measuring the delay of the received signal. In this paper we generalize the estimation of the effect of the shot noise when obtaining the phase delay with an arbitrary number of points in the Discrete Fourier Transform, extending and generalizing the analysis done in previous works
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Quantitative analysis of facial soft tissue using weighted cascade regression model applicable for facial plastic surgery Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-12-05 Ali Fahmi Jafargholkhanloo, Mousa Shamsi
Localization of facial landmarks plays an important role in the measurement of facial metrics applicable for beauty analysis and facial plastic surgery. The first step in detecting facial landmarks is to estimate the face bounding box. Clinical images of patients' faces usually show intensity non-uniformity. These conditions cause common face detection algorithms do not perform well in face detection
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DSRNet: Depth Super-Resolution Network guided by blurry depth and clear intensity edges Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-11-28 Hui Lan, Cheolkon Jung
Although high resolution (HR) depth images are required in many applications such as virtual reality and autonomous navigation, their resolution and quality generated by consumer depth cameras fall short of the requirements. Existing depth upsampling methods focus on extracting multiscale features of HR color image to guide low resolution (LR) depth upsampling, thus causing blurry and inaccurate edges
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A dual fusion deep convolutional network for blind universal image denoising Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-11-12 Zhiyu Lyu, Yan Chen, Haojun Sun, Yimin Hou
Blind image denoising and edge-preserving are two primary challenges to recover an image from low-level vision to high-level vision. Blind denoising requires a single denoiser can denoise images with any intensity of noise, and it has practical utility since accurate noise levels cannot be acquired from realistic images. On the other hand, edge preservation can provide more image features for subsequent
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ClGanNet: A novel method for maize leaf disease identification using ClGan and deep CNN Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-11-04 Vivek Sharma, Ashish Kumar Tripathi, Purva Daga, Nidhi M., Himanshu Mittal
With the advancement of technologies, automatic plant leaf disease detection has received considerable attention from researchers working in the area of precision agriculture. A number of deep learning-based methods have been introduced in the literature for automated plant disease detection. However, the majority of datasets collected from real fields have blurred background information, data imbalances
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Individual tooth segmentation in human teeth images using pseudo edge-region obtained by deep neural networks Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-10-26 Seongeun Kim, Chang-Ock Lee
In human teeth images taken outside the oral cavity with a general optical camera, it is difficult to segment individual tooth due to common obstacles such as weak edges, intensity inhomogeneities and strong light reflections. In this work, we propose a method for segmenting individual tooth in human teeth images. The key to this method is to obtain pseudo edge-region using deep neural networks. After
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Image tone mapping based on clustering and human visual system models Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-10-26 Xueyu Han, Ishtiaq Rasool Khan, Susanto Rahardja
Natural scenes generally have very high dynamic range (HDR) which cannot be captured in the standard dynamic range (SDR) images. HDR imaging techniques can be used to capture these details in both dark and bright regions, and the resultant HDR images can be tone mapped to reproduce them on SDR displays. To adapt to different applications, the tone mapping operator (TMO) should be able to achieve high
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Are metrics measuring what they should? An evaluation of Image Captioning task metrics Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-10-14 Othón González-Chávez, Guillermo Ruiz, Daniela Moctezuma, Tania Ramirez-delReal
Image Captioning is a current research task to describe the image content using the objects and their relationships in the scene. Two important research areas converge to tackle this task: artificial vision and natural language processing. In Image Captioning, as in any computational intelligence task, the performance metrics are crucial for knowing how well (or bad) a method performs. In recent years
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Self-embedding reversible color-to-grayscale conversion with watermarking feature Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-09-26 Felix S.K. Yu, Yuk-Hee Chan, Kenneth K.M. Lam, Daniel P.K. Lun
This paper presents a self-embedding reversible color-to-grayscale conversion (RCGC) algorithm that makes good use of deep learning, vector quantization, and halftoning techniques to achieve its goals. By decoupling the luminance information of a pixel from its chrominance information, it explicitly controls the luminance error of both the conversion outputs and their corresponding reconstructed color
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Co-occurrence spatial-temporal model for adaptive background initialization in high-dynamic complex scenes Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-09-20 Wenjun Zhou, Yuheng Deng, Bo Peng, Sheng Xiang, Shun’ichi Kaneko
Background information is an important aspect of pre-processing for advanced applications in computer vision. The literature has made rapid progress in background initialization. However, background initialization still suffers from high-dynamic complex scenes, such as illumination change, background motion, or camera jitter. Therefore, this study presents a novel Co-occurrence Spatial-Temporal (CoST)
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Jointly sparse fast hashing with orthogonal learning for large-scale image retrieval Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-09-19 Honghao Xu, Zhihui Lai, Heng Kong
Hash learning is a hot topic since it can save storage space and perform fast retrieval. One of the most representative hashing methods is Supervised Discrete Hashing (SDH). However, there exist several problems in SDH. First, the potential of sparse feature extraction has been overlooked in the SDH-based methods. Second, SDH is incapable of preventing large information loss between the binary codes
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Blind quality-based pairwise ranking of contrast changed color images using deep networks Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-09-23 Aladine Chetouani, Muhammad Ali Qureshi, Mohamed Deriche, Azeddine Beghdadi
Next-generation multimedia networks are expected to provide systems and applications with top Quality of Experience (QoE) to users. To this end, robust quality evaluation metrics are critical. Unfortunately, most current research focuses only on modeling and evaluating mainly distortions across the pipeline of multimedia networks. While distortions are important, it is also as important to consider
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Multi-operator Image Retargeting based on Saliency Object Ranking and Similarity Evaluation Metric Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-09-19 Yingchun Guo, Dan Wang, Ye Zhu, Gang Yan
Image Retargeting (IR) technology is proposed to flexibly display images on various display devices while protecting the important content of the images undistorted. IR methods mainly use Salient Object Detection (SOD) to obtain important content, however, most existing SOD methods treat multiple salient objects with the same saliency degrees, which makes IR methods assign the same retargeting ratios
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Background-detail restoration image deraining network based on convolutional dictionary network Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-09-18 Junhao Tang, Guorui Feng
The heavy rain streaks can seriously fade the color of images and destroy the background texture information. Although the existing deraining methods perform well in rain removal, we find that most of them tend to favor rain removal or restoration. In this paper, we propose a background-detail restoration image deraining network based on convolutional dictionary network to achieve effective deraining
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Attention-based dual-color space fusion network for low-light image enhancement Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-09-14 Zhixiong Huang, Jinjiang Li, Zhen Hua, Linwei Fan
The low visibility and dull colors associated with low-light images are not only difficult to satisfy the photographer, but also hinder further visual tasks. In this study, we propose an attention-based dual-color space fusion network to enhance low-light images. By introducing HSV color space, the network can solve the problems of dim color and insufficient contrast existing in previous RGB color
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Multi-image transformer for multi-focus image fusion Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-09-14 Levent Karacan
Multi-Focus Image Fusion (MFIF) is an image enhancement task that fuses images in which different regions are in focus to achieve an all-in-focus image. In recent years, Generative Adversarial Networks (GANs)-based approaches have significantly improved the MFIF on Convolutional Neural Network (CNN) architectures. However, despite vision transformers (ViTs) achieving more successful results than CNNs
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YOLO-PAI: Real-time handheld call behavior detection algorithm and embedded application Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-09-07 Zuopeng Zhao, Tianci Zheng, Kai Hao, Junjie Xu, Shuya Cui, Xiaofeng Liu, Guangming Zhao, Jie Zhou, Chen He
In some special locations, phone calls are strictly prohibited due to the possibility of significant security hazards. To prevent and reduce this dangerous behavior, we implemented real-time detection of handheld phones on resource-limited devices and developed a novel target detection network called YOLO-PAI. Initially, a pruning algorithm is mainly used to optimize CSPDarknet53 to reduce the number
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Dual-branch Multi-scale Densely Connected Network for Image Splicing Detection and Localization Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-08-28 Jingyuan Zhang, Hongxia Wang, Peisong He
In the last two decades, numerous methods have been proposed for image tampering forensics, especially when deep learning is leveraged as a powerful feature extraction technique. However, existing deep learning-based methods are not efficient to jointly explore robust representations of tampering traces from both low-level forensics patterns and high-level forensics semantics. Besides, they become
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Multi-scale feature fusion network with local attention for lung segmentation Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-08-25 Yinghua Xie, Yuntong Zhou, Chen Wang, Yanshan Ma, Ming Yang
Computer-assisted medical care can benefit from the lung region segmentation method. Numerous methods provide end-to-end solutions, these methods employ convolution neural networks to segment lung regions from images. The low contrast, unpredictable appearance, and other problems in medical images have an effect on the accuracy of existing methods. In order to overcome the aforementioned issues, the
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EFFNet: Element-wise feature fusion network for defect detection of display panels Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-08-18 Feng He, Jiubin Tan, Weibo Wang, Shutian Liu, Yuemin Zhu, Zhengjun Liu
Online or real-time defect detection of display panels after array process is of paramount importance for quality control and yield rate improvement of products in display industry. However, owing to the limitation in feature representation, the performances of traditional defect detection methods are not satisfactory. This paper develops a novel element-wise feature fusion network (EFFNet) to solve
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An image compression and encryption scheme for similarity retrieval Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-08-19 Ke Meng, Yan Wo
With the development of cloud computing, people usually outsource encrypted images for saving storage and protecting privacy. However, traditional image encryption methods not only hinder the availability of images such as similarity retrieval, but also degrade the compression performance. To address this issue, we propose a retrievable image compression and encryption method(RICE). RICE takes into
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DAResNet Based on double-layer residual block for restoring industrial blurred images Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-08-06 Weiquan Zhang, Yihao Cao, Rui Zhang, Wensheng Zhang, Zhihua Cui
Image acquisition and restoration have always been an indispensable part of industrial production. Due to the harsh environment in which industrial cameras are used and problems such as network transmission loss, the images captured are not high definition enough, making image restoration particularly important. Existing image restoration methods have a large number of parameters and time-consuming
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Transfer Learning Dehazing Network by Gaussian Process Mapping Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-08-04 Xiaozhe Meng, Ruizhi Liu, Zhuo Su, Fan Zhou
Haze seriously interferes with the high-quality imaging process, resulting in a significant difference from a haze-free scene to a hazy status. This phenomenon makes dehazing challenging. To make real data dehazing effective, we propose a novel Transfer Learning Dehazing Network (TLDN) with Gaussian Process Mapping (GPM) for the single image dehazing in this paper. Under our observation, we believe
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A transformer-based network for perceptual contrastive underwater image enhancement Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-08-02 Na Cheng, Zhixuan Sun, Xuanbing Zhu, Hongyu Wang
Vision-based underwater image enhancement methods have received much attention for application in the fields of marine engineering and marine science. The absorption and scattering of light in real underwater scenes leads to severe information degradation in the acquired underwater images, thus limiting further development of underwater tasks. To solve these problems, a novel transformer-based perceptual
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Determination of Lagrange multipliers for interframe EZBC/JP2K Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-08-01 Yuan Liu, John W. Woods
Interframe EZBC/JP2K has been shown to be an effective fine-grain scalable video coding system. However, its Lagrange multiplier values for motion estimation of multiple temporal levels are not specified, and must be specified by the user in the config file in order to run the program. In this paper, we investigate how to select these Lagrange parameters for optimized performance. By designing an iterative
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Deep steerable pyramid wavelet network for unified JPEG compression artifact reduction Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-29 Yi Zhang, Damon M. Chandler, Xuanqin Mou
Although numerous methods have been proposed to remove blocking artifacts in JPEG-compressed images, one important issue not well addressed so far is the construction of a unified model that requires no prior knowledge of the JPEG encoding parameters to operate effectively on different compression-level images (grayscale/color) while occupying relatively small storage space to save and run. To address
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Optical flow-assisted multi-level fusion network for Light Field image angular reconstruction Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-29 Deyang Liu, Yifan Mao, Yan Huang, Liqun Cao, Yuanzhi Wang, Yuming Fang
Light Field (LF) imaging can record both the intensities and directions of light rays in a single exposure, which has received extensive attentions. However, the limited angular resolution becomes the primary bottleneck for the wide-spread applications of LF imaging. To this end, this paper proposes a novel optical flow-assisted multi-level fusion network for LF angular reconstruction. In our method
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PL-GNet: Pixel Level Global Network for detection and localization of image forgeries Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-23 Zenan Shi, Xuanjing Shen, Haipeng Chen, Yingda Lyu
Unlike most Image Forgery Detection and Localization (IFDL) methods that classify the tampered regions by local patch, the features from the whole image in the spatial and frequency domains are leveraged in this paper to classify each pixel in the image. This paper proposes a high-confidence pixel level global network called PL-GNet to combat real-life image forgery that commonly involves different
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RGB pixel n-grams: A texture descriptor Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-16 Fátima Belén Paiva Pavón, María Cristina Orué Gil, José Luis Vázquez Noguera, Helena Gómez-Adorno, Valentín Calzada-Ledesma
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AdaTriplet-RA: Domain matching via adaptive triplet and reinforced attention for unsupervised domain adaptation Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-16 Xinyao Shu, Shiyang Yan, Zhenyu Lu, Xinshao Wang, Yuan Xie
Unsupervised domain adaptation (UDA) is a transfer learning task where the annotations of the source domain are available, but only have access to the unlabelled target data during training. Previous methods minimise the domain gap by performing distribution alignment between the source and target domains, which has a notable limitation, i.e., at the domain level, but neglecting the sample-level differences
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Joint adjustment image steganography networks Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-15 Le Zhang, Yao Lu, Tong Li, Guangming Lu
Image steganography aims to achieve covert communication between two partners utilizing stego images generated by hiding secret images within cover images. Existing deep image steganography methods have been rapidly developed in this area. Such methods, however, usually generate the stego images and reveal the secret images using one-process networks, lacking sufficient refinement in these methods
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A coarse-to-fine multi-scale feature hybrid low-dose CT denoising network Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-13 Zefang Han, Hong Shangguan, Xiong Zhang, Xueying Cui, Yue Wang
With the growing development and wide clinical application of CT technology, the potential radiation damage to patients has sparked public concern. However, reducing the radiation dose may cause large amounts of noise and artifacts in the reconstructed images, which may affect the accuracy of the clinical diagnosis. Therefore, improving the quality of low-dose CT scans has become a popular research
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No-reference blurred image quality assessment method based on structure of structure features Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-13 Jian Chen, Shiyun Li, Li Lin, Jiaze Wan, Zuoyong Li
The deep structure in the image contains certain information of the image, which is helpful to perceive the quality of the image. Inspired by deep level image features extracted via deep learning methods, we propose a no-reference blurred image quality evaluation model based on the structure of structure features. In spatial domain, the novel weighted local binary patterns are proposed which leverage
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First-order primal-dual algorithm for image restoration corrupted by mixed Poisson–Gaussian noise Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-11
The total variation infimal convolution (TV-IC) model combining Kullback–Leibler and ℓ2-norm data fidelity term works well for the inverse problem of mixed Poisson–Gaussian noise. Most existing algorithms for solving the TV-IC model rely on the Newton method to solve a nonlinear optimization subproblem, which inevitably increases the computation burden. In this study, we apply the first-order primal–dual
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PIMnet: A quality enhancement network for compressed videos with prior information modulation Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-11
In this paper, we propose a quality enhancement network for compressed videos, named as PIMnet, which can effectively use the spatio-temporal information of multiple frames to improve the video quality. The main idea of PIMnet is to use the Quantization Parameter (QP) and Delta Picture Order Count (ΔPOC) of multiple input frames to modulate the network, where QP can reflect the quality of frames and
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Scale-progressive Multi-patch Network for image dehazing Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-11
Image dehazing is a classical vision task, which aims to recover a clean image from a hazy one. Previous dehazing methods usually follow a coarse-to-fine architecture to mine clean features by introducing generic CNNs components. However, this manner usually results in undesirable model complexity and computational burden. In this work, we present a Scale-progressive Multi-patch Network (SPM-Net) to
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Fluorescence microscopy images denoising via deep convolutional sparse coding Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-11
Fluorescence microscopy images captured in low light and short exposure time conditions are always contaminated by photons and readout noises, which reduce the fluorescence microscopy images quality. In most cases, this kind of noise can be modeled as Poisson–Gaussian noise. Correspondingly, its denoising task has always been a hot but challenging topic in recent years. In this paper, by integrating
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Dual attention guided multi-scale fusion network for RGB-D salient object detection Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-11 Huan Gao, Jichang Guo, Yudong Wang, Jianan Dong
While recent research on salient object detection (SOD) has shown remarkable progress in leveraging both RGB and depth data, it is still worth exploring how to use the inherent relationship between the two to extract and fuse features more effectively, and further make more accurate predictions. In this paper, we consider combining the attention mechanism with the characteristics of the SOD, proposing
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Multi-scale deep feature fusion based sparse dictionary selection for video summarization Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-11 Xiao Wu, Mingyang Ma, Shuai Wan, Xiuxiu Han, Shaohui Mei
The explosive growth of video data constitutes a series of new challenges in computer vision, and the function of video summarization (VS) is becoming more and more prominent. Recent works have shown the effectiveness of sparse dictionary selection (SDS) based VS, which selects a representative frame set to sufficiently reconstruct a given video. Existing SDS based VS methods use conventional handcrafted
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Surprise-based JND estimation for perceptual quantization in H.265/HEVC codecs Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-11 Hongkui Wang, Li Yu, Hailang Yang, Haifeng Xu, Haibing Yin, Guangtao Zhai, Tianzong Li, Zhuo Kuang
Just noticeable distortion (JND), reflecting the perceptual redundancy directly, has been widely used in image and video compression. However, the human visual system (HVS) is extremely complex and the visual signal processing has not been fully understood, which result in existing JND models are not accurate enough and the bitrate saving of JND-based perceptual compression schemes is limited. This
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HAFFseg: RGB-Thermal semantic segmentation network with hybrid adaptive feature fusion strategy Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-11
RGB-Thermal (RGB-T) semantic segmentation provides the pixel-level prediction of surrounding environments for autonomous vehicles and mobile robots in harsh conditions such as insufficient illumination and severe weather. Existing RGB-Thermal semantic segmentation networks with two modalities feature extraction branches and insufficient feature fusion strategy limit their segmentation performance.
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Low-light image enhancement based on virtual exposure Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-11 Wencheng Wang, Dongliang Yan, Xiaojin Wu, Weikai He, Zhenxue Chen, Xiaohui Yuan, Lun Li
Under poor illumination, the image information captured by a camera is partially lost, which seriously affects the visual perception of the human. Inspired by the idea that the fusion of multiexposure images can yield one high-quality image, an adaptive enhancement framework for a single low-light image is proposed based on the strategy of virtual exposure. In this framework, the exposure control parameters
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Multi-scale graph neural network for global stereo matching Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-10 Xiaofeng Wang, Jun Yu, Zhiheng Sun, Jiameng Sun, Yingying Su
Currently, deep learning-based stereo matching is solely based on local convolution networks, which lack enough global information for accurate disparity estimation. Motivated by the excellent global representation of the graph, a novel Multi-scale Graph Neural Network (MGNN) is proposed to essentially improve stereo matching from the global aspect. Firstly, we construct the multi-scale graph structure
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Super-resolution image visual quality assessment based on structure-texture features Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-08 Fei Zhou, Wei Sheng, Zitao Lu, Bo Kang, Mianyi Chen, Guoping Qiu
Assessing the visual quality of super-resolution images (SRIs) is crucial for advancing algorithm development, but it remains an unsolved problem. In this paper, we present a novel reduced-reference image quality assessment (RR-IQA) method specifically suited for evaluating SRIs. Our approach leverages information from the input low-resolution (LR) image as a reference signal to extract features that
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Multi-label adversarial fine-grained cross-modal retrieval Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-07 Chunpu Sun, Huaxiang Zhang, Li Liu, Dongmei Liu, Lin Wang
Most supervised cross-modal approaches transform features into a common representation space in which semantic similarity can be measured directly. How- ever, there exist modal specific features in the common semantic space and most methods can not fully eliminate them. In order to bridge the semantic gap and eliminate modal specific features, we propose a novel Multi-label Adversarial Fine-grained
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MR image restoration and segmentation via denoising deep adversarial network for blood vessels accurate diagnosis Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-07 Pandia Rajan Jeyaraj, Edward Rajan Samuel Nadar
Segmenting the hepatic and portal veins is a difficult task, since it has multiple distortions. For effective restoration and to minimize distortions, a multi-stage deep adversarial learning network was proposed. The proposed network provides high reliability in segmenting hepatic and portal veins from distorted Magnetic Resonance (MR) images. The Proposed network directly considers the variation in
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Physics-based optical flow estimation under varying illumination conditions Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-07 Xiaoxin Liao, Zemin Cai, Jun Chen, Tianshu Liu, Jian-huang Lai
The physics-based optical flow (PBOF) model was derived from typical flow visulizations and it provided a more solid physical foundation for optical flow calculation. However, like many recent variational optical flow methods, it is not formulated to deal with the effect of illumination variation. In this paper, we propose a new efficient illumination-invariant optical flow estimation method, which
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Multi-level adversarial attention cross-modal hashing Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-07 Benhui Wang, Huaxiang Zhang, Lei Zhu, Liqiang Nie, Li Liu
Deep cross-modal hashing has made great progress in recent years due to the development of deep learning and efficient hashing algorithms. However, most of the existing methods only focus on the feature distribution between modalities, and ignore the fine grain information in each modality. To solve this problem, we propose a multi-level adversarial attention cross-modal hashing (MAAH). First, we design
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Soccer line mark segmentation and classification with stochastic watershed transform Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-08 Daniel Berjón, Carlos Cuevas, Narciso García
Augmented reality applications are beginning to change the way sports are broadcast, providing richer experiences and valuable insights to fans. The first step of augmented reality systems is camera calibration, possibly based on detecting the line markings of the playing field. Most existing proposals for line detection rely on edge detection and Hough transform, but radial distortion and extraneous
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Dynamic Pruning of Regions for Image–Sentence Matching Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-07 Jie Wu, Weifeng Liu, Leiquan Wang, Xiuxuan Shen, Yiwei Wei, Chunlei Wu
Image–sentence matching is becoming increasingly essential in the integrated understanding of vision and language. Prior approaches apply a pre-trained detection model to extract region features and explore fine-grained relationships between image and sentence by aggregating the similarities of all region–word pairs. However, all images are represented by the same number of regions, regardless of their
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Quantum image scaling with applications to image steganography and fusion Signal Process. Image Commun. (IF 3.5) Pub Date : 2023-07-07 Nianqiao Li, Fei Yan, Salvador E. Venegas-Andraca, Kaoru Hirota
A quantum hue, saturation, and lightness model is proposed in which a triple qubit sequence (QHTS) is encoded and used as a data model for the implementation of quantum image scaling. The preparation and retrieval of QHTS images is presented, in which only q+2 qubits (where q is the bit depth) are required to encode color information while retaining relevant HSL image features and operability. A conventional