We propose accelerated implementations of bilateral filter (BF) and nonlocal means (NLM) called color-compressive bilateral filter (CCBF) and color-compressive nonlocal means (CCNLM). CCBF and CCNLM are random filters, whose Monte-Carlo averaged output images are identical to the output images of conventional BF and NLM, respectively. However, CCBF and CCNLM are considerably faster because the spatial

We propose a video stabilization algorithm based on the rotation of a virtual sphere. Unlike traditional video stabilization algorithms relying on two-dimensional motion models or reconstruction of (3D) camera motions, the proposed virtual sphere model stabilizes video by projecting each frame onto the sphere and performing corrective rotations. Specifically, matching feature points between two adjacent

Single-image super-resolution (SISR) refers to reconstructing a high-resolution image from given low-resolution observation. Recently, convolutional neural network (CNN)-based SISR methods have achieved remarkable results in terms of peak-signal-to-noise ratio and structural similarity measures. These models use pixel-wise loss functions to optimize their models, which results in blurry images. However

Single-pixel camera is developed to mitigate the constraints faced by the conventional cameras especially in invisible wavelengths and low light conditions. Nyquist–Shannon theorem requires as many measurements as the image pixels to reconstruct images flawlessly. In practice, obtaining more measurements increases the cost and acquisition time, which are the major drawbacks of single-pixel imaging

Haze scatters light transmitted in the air and reduces image quality, which greatly decreases the interpretability and intelligibility of an image. To solve these problems, we propose an improved real-time image dehazing algorithm based on dark channel prior and fast weighted guided filtering. First, the image is divided into dark areas and bright areas by the K-means clustering algorithm, and the

Image steganography has emerged as a method of hiding secret data within an image file to ensure the security of the transmitted data. In this study, we propose an architecture named MDRSteg to unobtrusively hide a large-size image in another image based on a residual neural network with dilated convolution and multi-scale fusion. The architecture consists of an embedding network to hide the secret

Road accidents continue to increase and cause intense fatalities. Studies based on manual and/or semiautomatic methods remain unable to reliably track the random behavior of pedestrians. Hence, we fill this gap by developing an automatic Bayesian and cognitive model (ABC model) for pedestrian behavior analysis. We propose a full and complete computer vision process composed of two images processing

Transfer learning provides a useful solution to learn a new conceptual domain from few examples, which exploits prior knowledge from a related domain. We proposed a simple and yet effective transfer learning method for image classification that constructs an activation ensemble generative adversarial net (AE-GAN) to transfer knowledge from one dataset to another. The AE-GAN is mainly composed of three

Visual target tracking is an important function in real-time video monitoring application, whose performance determines the implementation of many advanced tasks. At present, Siamese-network trackers based on template matching show great potential. It has the advantage of balance between accuracy and speed, due to the pre-trained convolutional network to extract deep features for target representation

Pedestrian occlusion, variations in the cross-view angle, and the appearances of pedestrians significantly hinder person reidentification (ReID). A dual attention and part drop network (DAPD-Net) for person ReID is proposed. The dual attention module enables the deep neural network to focus on the pedestrian in the foreground of a given image and weakens background perturbance. It can speed up learning

Guided (joint) image filters play an important role in many computer vision and image processing applications. The main principle behind these filters is transferring the structural information from a guidance image to an input one. However, in practice, the structures between the two images are not always consistent. As a result, the filtering outputs become sensitive to outliers, which easily leads

Color-texture image segmentation remains a challenging problem due to extensive color-texture variability. Thus, the limited prior knowledge that is expressed by pairwise constraints can be exploited to guide the segmentation process. We propose a new semisupervised method by combining constrained feature selection and spectral clustering (SC) to perform color-texture image segmentation. The pairwise

Lung cancer has caused the most cancer deaths in the past several years. Benign–malignant lung nodule classification is vital in lung nodule detection, which can help early diagnosis of lung cancer. Most existing works extract the features of chest CT images using the well-designed networks, which require substantial effort of experts. To automate the manual process of network design, we propose an

Aiming at the problems of color distortion, nonuniform illumination, and low contrast caused by degradation of underwater images, an underwater image enhancement method (MSFF-GAN) based on generative adversarial network was proposed. A multiscale featured fusion generator is designed, which improves the ability to use different scale features of the model and ensures that the generated image retains

Multispectral image compression can considerably reduce the volume of data and promote their application. However, conventional single-scale compression schemes, such as JPEG2000 and three-dimensional set partitioning in hierarchical tree (3D-SPIHT), do not accurately preserve the features of images due to the complex features of multispectral images. A compression framework based on adaptive multiscale

In recent years, a number of detectors have been proposed to improve the accuracy and speed of object detection tasks. However, poor detection performances for small objects and difficulties in optimizing deep networks remain critical challenges for object detection. We try to tackle these problems in two ways. First, we propose an innovative cross-fusion block (CFB) module that can enhance the representational

Geospatial object detection in remote sensing images is a challenging subject since objects in remote sensing images are dense, multioriented, and multiscale. We present an attention network for object detection in remote sensing images. Through channel attention and spatial attention, the framework pays more attention to important channels and emphasizes position information of objects. Meanwhile

Image encryption plays an essential role in the community of image security. Most existing image encryption approaches adopt a permutation–diffusion scheme to permute pixel positions and change pixel values separately. One limitation of this scheme is that it has a high risk of being cracked. To solve this problem, we propose an approach that jointly permutates and diffuses (JPD) the pixels in a color

We propose the fractional-order total variation (TV) algorithm with nonlocal self-similarity for image reconstruction in compressed sensing to alleviate texture details deterioration and eliminate staircase artifacts, which results from the TV algorithms. The Grünwald–Letnikov fractional-order differential operators, which consider more neighboring image pixels and use four different directions to

Traditional super-resolution techniques are generally presented as optimization problems with variations in the choice of optimization methods and cost functions. Even for the overdetermined cases, the problem is ill-conditioned. The situation is worsened when considering underdetermined cases with unknown regions due to occlusions or lack of data. Deep learning-based methods have shown promise in

With modern science and technology development, vehicles are equipped with intelligent driver assistant systems, of which lane detection is a key function. These complex detection structures (either wide or deep) are investigated to boost the accuracy and overcome the challenges in complicated scenarios. However, the computation and memory storage cost will increase sharply, and the response time will

We propose to rely on the wave equation for the detection of repeatable keypoints invariant up to image scale and rotation and robust to viewpoint variations, blur, and lighting changes. The algorithm exploits the properties of local spatial–temporal extrema of the evolution of image intensities under the wave propagation to highlight salient symmetries at different scales. Although the image structures

This is a list of reviewers who served the Journal of Electronic Imaging in 2020.

We propose a robust license plate detection and recognition (LPDR) framework with automatic rectification. We explore the YOLOv2 object detector based on deep learning and train it to detect license plates (LPs) effectively. The LPs in natural scene images tend to be tilted and distorted because of the shooting angle or the geometric deformation of LPs. To solve the problem in which the LP tilt and

Person reidentification (ReID) requires the discriminative features of an entire pedestrian image to handle the problems of inaccurate person bounding box detection, background confusion, and occlusion. Many recent person ReID methods have attempted to learn the feature information of an entire pedestrian image through parts feature representations, but it is often too time consuming. Person ReID relies

Document image binarization is the process in which pixels in a document image are classified into two groups—foreground and background. This process becomes challenging when it deals with various degradation and noise present in the images. In the recent past, it has been observed that researchers are relying on deep learning-based approaches to solve the problem of document image binarization. Of

A brightness preservation dehazing algorithm based on the atmospheric scattering model is proposed. We introduce a novel pixel-patch-wise transmission. It not only can keep the scene radiance depth from becoming underestimated, it also preserves image color and brightness. First, based on the correlation of saturation and brightness in hue, saturation, and intensity color space, a pixel-wise transmission

Natural images inevitably suffer from spatially variant blur caused by the relative motion between a camera and objects. We present an effective and efficient patch-wise edge-enhanced image regularization and a robust kernel similarity constraint to perform an accurate kernel estimation from coarse-to-fine iterations. The proposed adaptive regularization introduces a gradient magnitude penalty function

Video super-resolution (VSR) is an image restoration task, aiming to reconstruct a high-resolution (HR) video from its down-sampled low-resolution (LR) version. Convolutional neural networks (CNNs) have been applied to VSR successfully. Explicit motion estimation and motion compensation (ME&MC) module is commonly used in the previous CNNs-based methods to better exploit input frames’ temporal similarity

Recently, deep learning has developed rapidly and made great improvements in facial expression recognition. However, deep learning has black box properties that lead to poor interpretability of the results. Differentiable programming provides a new aspect to balance the interpretability and convenience of deep learning. It provides a way to solve sparse coding problem that has solid mathematical foundations

Text-to-image generation can be widely applied in various fields, such as scene retrieval and computer-aided design. The existing approaches can generate realistic images from simple text descriptions, whereas rendering images from complex text descriptions is still not satisfactory for practical applications. To generate accurate high-resolution images from given complex texts, we proposed an attention-enhancing

We propose a network structure for action recognition that is capable of extracting multi-scale temporal representations of actions. The key of the network is to combine a multi-scale temporal pooling module with a dense connection module, called multi-scale temporal pooling dense convolutional network (MTPDNet). The multi-scale temporal pooling module consists of multiple temporal scale levels. At

The detection of plant organs is an important research field of plant recognition area. However, due to the lack of database of plant organs, the application of convolutional neural network-based object detection on plant species is very limited. A database of plant organs for deep learning-based object detection is constructed. A huge number of plant images are clawed using specific keywords through

Convolutional neural networks (CNN) have immensely promoted the development of face recognition (FR) technology. In order to achieve global accuracy, CNN models tend to be deeper or multiple local facial patch ensembles, leading to excessive amounts of calculation. We address these deep FR problems and propose a lightened deep learning framework under an open-set protocol to achieve a good classification

Subtraction techniques are used to distinguish moving objects or foregrounds that are being tracked from a static background. To prevent possible local misclassifications, the subspace spanned by low-rank features computed from a multilinear singular value decomposition (MLSVD), can be used to filter out noises or gradual changes from the background. However, as it is prohibitively expensive to compute

As an instance-level recognition problem, the key to effective vehicle re-identification (Re-ID) is to carefully reason the discriminative and viewpoint-invariant features of vehicle parts at high-level and low-level semantics. However, learning part-based features requires a laborious human annotation of some factors as attributes. To address this issue, we propose a region-aware multi-resolution

Several leading-edge applications such as pathology detection, biometric identification, and face recognition are based mainly on blob and line detection. To address this problem, Eigen value computing has been commonly employed due to its accuracy and robustness. However, Eigen value computing requires a raised computational processing, intensive memory data access, and data overlapping, which involve

Steganography is a technique applied to ensure secure communication. It is challenged by visual observation or statistical analysis to ascertain whether a message is hidden within a cover medium. Several steganographic methods have been proposed for palette-based images. These methods maintain image quality but cannot resist some statistical and visual attacks. To overcome this problem, two parity

It is very challenging to recognize the information of occluded Tibetan document pages due to the lack of digitization and their long-term storage. Multiple pages are stuck, and textual characters are occluded with each other, which causes difficulties in restoration. Due to the large size of Tibetan documents, it is impossible to separate and repair these occluded pages by professionals. Therefore

Semantic segmentation of three-dimensional (3D) scenes is a challenging task in 3D scene understanding. Recently, deep learning-based segmentation approaches have made significant progress. A multiscale spatial context feature learning is used in an end-to-end approach for 3D point cloud semantic segmentation. Furthermore, a local feature fusion learning block is then introduced to the hidden layers

Gender classification has found many useful applications in the broader domain of computer vision systems including in-cabin driver monitoring systems, human–computer interaction, video surveillance systems, crowd monitoring, data collection systems for the retail sector, and psychological analysis. In previous studies, researchers have established a gender classification system using visible spectrum

Rain streaks attached to a camera may seriously affect the visibility of the background and considerably degrade image quality. We handle this problem by removing rain streaks to convert a rainy image into a clean one. This creates a problem in that the information with respect to the background of the occluded parts is close to being lost for the most part. To remove rain streaks from a single image

Infrared-visible cross-modality person re-identification (IV-ReID) is a challenging task that aims to match infrared person images with visible person images of same identity. The person images of two modalities are captured by visible cameras and infrared cameras, respectively. Due to the variation between two modalities, most existing methods tend to extract common features of different modalities

Object tracking is challenging due to unconstrained variations in the target’s appearance and complex environmental variations. Appearance models based on a single cue are inefficient in addressing the various tracking challenges. To address this, we propose a discriminative visual tracking approach in which complementary multicue features viz. RGB cue and histogram of gradient are integrated to build

Blind deconvolution is known as a challenging low-level vision problem due to the diverse blur scenarios in real-world imaging. Another attempt is made with critical thoughts on existing image priors for nonparametric blur kernel estimation, proposing an alternative approach to blind deconvolution via complementarily structure-aware image smoothing (CSIS). Similar to most state-of-the-art blind deblurring

The fusion of depth acquired actively with the depth estimated passively proved its significance as an improvement strategy for gaining depth. This combination allows us to benefit from two sources of modalities such that they complement each other. To fuse two sensor data into a more accurate depth map, we must consider the limitations of active sensing such as low lateral resolution while combining

It is a challenge to remove the cluttered background in research of hand gesture images. The popular method, image semantic segmentation, is still not efficient enough to deal well with fine-grained image background removal due to insufficient training samples. We are the first to propose a background removal method based on a conditional generative adversarial network (CGAN). With CGAN, our method

Current self-supervised monocular methods only learn effectively by imposing consistency constraints without relying on any geometric constraints or ground truth depth constraints, which makes the accuracy of the estimation result suboptimal. Compared with the monocular algorithm, the stereo matching network usually follows the geometric process of the traditional stereo algorithm, which makes the

We describe the improvements of the content-based image retrieval (CBIR) system using a fuzzy class membership for the natural-color images. The fuzzy class membership-based retrieval (CMR) framework has shown promising improvements on texture databases by exploiting confidence in classification using a multilayer perceptron (MLP). CMR is known to improve the average precision of retrieval along with

Tracking objects with the scale variations quickly is a challenging problem in visual tracking. Most existing methods estimate the scale of the object with an exhaustive search strategy, which needs large calculations but with less improvement. We propose to use twofold weighted phase correlation with the kernel (WPCK) to simultaneously estimate the translation and scale for the object during tracking

A motion energy image (MEI) is a spatial template that collapses regions of motion into a single image in which more moving pixels are brighter than others. The forward single-step history image (fSHI) is a spatiotemporal template that shows the presence and direction of motion. Each video can be described using these templates. Recently, the popularity of deep learning architectures for human activity

Biometric identification from three-dimensional (3-D) face surface characteristics has become popular. Traditional face recognition methods have achieved very high recognition accuracy under controlled environments. However, 3-D face recognition technology still faces a great challenge for facial expressions and missing data caused by pose variations or occlusions. The mesh-based scale-invariant feature

Development of computer vision technologies has been widely used to increase the level of agricultural intelligence. Crop counting, an application of image counting, plays a fundamental role in agricultural information automation. However, the complex cotton field environment is likely lead to incorrect detection of the target position or fragmentation of the segmentation results, resulting in a decrease

Color constancy algorithms can provide us with illuminant invariant descriptions for a scene, and it is often accomplished by illuminant estimation. Most statistics-based methods estimate the illuminant color from the information provided by all pixels of an image. However, this research reveals that, for most images, the color of many pixels is quite different from the illuminant, and these pixels

Multiview learning is an important method and widely used for feature fusion in the fields of image process or big data analysis. Determining how to integrate compatible and complementary information from multiple views is a crucial and challenging task. We present a multiview feature fusion optimization method for image retrieval based on matrix correlation. This method first extracts four view features

In recent years, helping individuals with color vision deficiency to distinguish confusing colors in digital images, which is called Daltonization, is a hot topic. However, a number of Daltonization methods have a color reduction problem that causes unnatural image colors for normal color vision observers and those with anomalous trichromatic color vision deficiencies. A color-enhancing algorithm is

Predicting bounding box with higher intersection over union (IoU) is one of the most important issues in many computer vision tasks. The ℓn-norm loss and IoU-based loss are two conventional approaches to guide a training process in recent methods. However, the optimization direction of ℓn-norm loss is not exactly the same as maximizing the metric. In addition, IoU-based loss suffers from some inevitable

Crowd density estimation is an important part of intelligent crowd monitoring. However, there are still many problems in density estimation due to the complexity of crowd scenes. Aiming at the high-density scenes with varied scales, we present a method based on cross-adversarial loss and global feature for crowd counting, so as to achieve the purpose of capturing more feature details and reducing the

Object detection for blind zones is critical to ensuring the driving safety of heavy trucks. We propose a scheme to realize object detection in the blind zones of heavy trucks based on the improved you-only-look-once (YOLO) v3 network. First, according to the actual detection requirements, the targets are determined to establish a new data set of persons, cars, and fallen pedestrians, with a focus

Human action recognition based on skeleton currently has attracted a wide range of attention. The structure of skeleton data exists in the form of graph, thus most researchers use graph convolutional networks (GCN) to model skeleton sequences. However, the graph convolution network shares the same weight for all neighbor nodes and relies on the connection of graph edges. We introduce a method, a spatial–temporal