Action recognition has wide applications in fields such as human–computer interaction, virtual reality, and robotics. Since human actions can be represented as a sequence of skeleton graphs, approaches based on graph neural networks (GNNs) have attracted considerable attention in the research action recognition. Recent studies have demonstrated the effectiveness of two-stream GNNs in which discriminative

Single-image dehazing is a critical problem since haze existence degrades the quality of images and hinders most advanced computer vision tasks. Early methods solve this problem via the atmospheric scattering model, which estimate the intermediate parameters and then recover a clear image by low-level priors or learning on synthetic datasets. However, these model-based methods do not hold in various

Electro-optical (EO) imaging sensors are employed for object detection, recognition, identification, and tracking. The observation range of EO systems is defined as the maximum distance at which a target of interest can be seen and perceived. The performance of these systems deteriorates under degraded weather conditions, which severely affects the image quality and thus the observation range of EO

Removing spatially variant motion blur from a blurry image is a challenging problem as image blur can be complicated and difficult to model accurately. Recent progress in deep neural networks suggests that kernel-free single image deblurring can be achieved, but questions about deblurring performance persist. To improve performance, we proposed a deep convolutional neural network to restore a sharp

Underexposed images are usually low in brightness and contrast, which degrade the performance of many computer vision algorithms. To solve the problem of overexposing areas that tend to be normal while recovering dark areas in low-light image enhancement tasks, we propose an image-to-patch enhancement model and design a lightweight convolutional neural network called PatchNet. Specifically, the new

We proposed a motion-perceptive deformable alignment network that introduces a pre-computed optical flow to improve the motion perception of the deformable alignment process. The pre-computed flows share the burden of the learned offsets for motion estimation while the flexibility of the deformable convolutional network is maintained. In addition, a motion-adaptive pyramid structure in which the features

Facial expression recognition (FER) has applications in many scenarios, making it a valuable research direction. However, due to the uncertainty of real-world images, their recognition accuracy is not satisfying. To deal with this problem, we propose a two attention modules-based network that uses two different attention modules to extract features. The main classifier uses the self-attention module

We propose an optimization framework for interactive image segmentation (IIS) that operates in bilateral space to achieve robust object extraction and instant visual feedback. More specifically, we first resample an input image using a regular bilateral grid with a resolution that is typically coarser than the input image to reduce the complexity of subsequent IIS tasks. We then design a Markov random

With the development of feature extraction technique, one image object can be represented by multiple heterogeneous features from different views that locate in high-dimensional space. Multiple features can reflect various characteristics of the same object; they contain compatible and complementary information among each other, integrating them together used in the special image processing application

Action recognition makes significant contributions to sports video analysis, especially for athletes’ training evaluations. For sports video analysis, the action information is mainly conveyed by human body parts’ temporal movement, and each of the parts has a unique importance to the action representation. Aiming to involve this point in action recognition, we propose a part-attention spatio-temporal

A convolutional capsule network that characterizes small objects previously has shown remarkable performance in small object classification. We extend the idea of capsules to the image denoising task and combine it with the generative adversarial network to develop a generative adversarial capsule network (DeCapsGAN). Both the generator and discriminator adopt the capsule network architecture. The

The high-efficiency video coding (HEVC) standard can reduce the bit rate of compression video. However, the compression efficiency of HEVC is still challenging current internet bandwidth. This problem can be solved by reducing the number of video frames encoded by HEVC and reconstructing the reduced frames that rely on frame rate up conversion (FRUC). Unfortunately, most existing FRUC methods need

Local features are widely applied in person re-identification (ReID) because of the rich fine-grained information. However, there are two problems existing in the local feature methods: the local regions are not accurate and the contextual information between different regions is not considered. To solve these problems, we propose a person ReID method based on attention clustering and long short-term

Nowadays, many RGB-D saliency detection models have been proposed, and they are conducted based on the pixel-level depth data obtained by active depth sensors or dense stereo matching algorithms in advance. However, the pixel-level depth maps may have a negative impact on saliency ranking, especially when they are inconsistent or invalid within the object area. It can be found that people tend to observe

The ArUco marker is one of the most popular squared fiducial markers using for precise location acquisition during autonomous unmanned aerial vehicle (UAV) landings. This paper presents a novel method to detect, recognize, and extract the location points of single ArUco marker based on convolutional neural networks (CNN). YOLOv3 and YOLOv4 networks are applied for end-to-end detection and recognition

Recently, deep learning has become a rapidly developing tool in the field of image fusion. An innovative image fusion method for fusing infrared images and visible-light images is proposed. The backbone network is an autoencoder. Different from previous autoencoders, the information extraction capability of the encoder is enhanced, and the ability to select the most effective channels in the decoder

Purpose: Recent research has been producing an important effort in encoding of digital image content. Most of the adopted paradigms only focus on local features and lack of information about location and relationships between them. Approach: To fill this gap, we propose a framework built on three cornerstones. First, the adoption of attributed relational scale-invariant feature transform regions graph

Supervised single-image super-resolution (SISR) reconstruction models are trained with both low-resolution images (ILR) and their corresponding high-resolution images (IHR). During the training process, ILR are obtained by performing a bicubic downscaling on their IHR counterparts. This means that the model learns an inverted version of the bicubic downscaling, resulting in less realistic images that

Visual object tracking, which aims to estimate the position of an arbitrary target in a video sequence automatically, has drawn great attention in recent years. Many efforts have been made regarding this topic. The Siamese network, with a balanced accuracy and speed, has achieved great success. The Siamese network consists of two branches: one for the target image and the other for the search image

Compared with the improvement of model structure in the field of object detection, the imbalanced problems in the training process have received less attention, but it is also one of the main reasons affecting its performance. We mainly analyze the imbalanced problems that occur in different stages of the network training process and propose a more balanced network called Balanced-RetinaNet, which

In recent years, to save bandwidth and storage space, images are usually compressed to reduce data volume, which leads to the loss of image details and affects the super-resolution (SR) performance. SR of compressed images is a key technique for addressing this problem. We propose the compressed-image super-resolution using enhanced attention network (CISREAN). First, the task is divided into two subtasks:

The feedback mechanism method of simulating the biological vision system has not been widely used in deep learning dehazing algorithms. To alleviate the difficulty of feature interaction, we combine the feedback mechanism with dense skip connections to fuse features of different levels in a dehazing network. Inspired by the feedback network in which previous network layers can have access to rich information

Palmprint region of interest (ROI) extraction under unconstrained conditions is an unavoidable problem in realizing palmprint recognition. However, the diversity of palm size, posture, illumination, and background undoubtedly poses a great challenge. Therefore, in response to this problem, we collected about 30,000 unconstrained palmprint images from 100 people using five mobile phones with the flash

The vulnerability of face recognition-based unlocking systems to spoofing attacks is a serious problem. With the development of science and technology, face pictures on the Internet are particularly easy to obtain and recreate. A face spoofing attack happens when someone fakes a face and tries to pass the verification of the face recognition system, which seriously threatens the security of users’

With the rapid development of deep learning, iris recognition methods based on deep learning are constantly being proposed. These methods generally consist of iris segmentation and normalization to more accurately locate iris regions and reduce the impact of iris feature changes caused by pupil expansion. We propose a non-segmentation (NS) iris recognition framework based on a deep learning classification

We introduced an efficient technique to suppress speckle noise in medical ultrasound images while maintaining low computational complexity. The medical ultrasound image is divided into overlapping subimages, and Lanczos decomposition is then applied to the average Hermitian covariance matrix of all subimages. The resultant orthonormal vectors are used for filtering speckle noise through orthogonal

The guest editors introduce the Special Section on Advances in Urban Imaging and Applications.

Nowadays, with the growing maturity of action capture technology and the increasing requirements of the game and film industry for action and shooting level, action capture technology has become more and more widely applied. In addition, virtual shooting is widely applied to many TV programs and film production. For example, the movie “The Lion King” released in 2019 was completed using virtual shooting

Noise reduction is an essential preprocess in applications of complex-valued images. A method of complex-valued image denoising based on complex-valued dictionary learning and an adaptive complex-valued dictionary filter (OCDL-ACDF) is proposed. Our dictionary is first trained by the online dictionary learning method. Then, to further reduce the noise contained in the dictionary atoms, we design a

We propose a dual-band fusion method using multiscale visual saliency extraction based on spatial weight matrix. There are two main contributions in this paper. The first major contribution is to use the local window based on the gray distance of the spatial weight for saliency extraction. Second, to emphasize those potential targets and details with different sizes from source images, we based on

We present an effective blind image deblurring method based on the reweighted L1 norm prior. The prior is motivated by that the traditional L1 norm highly depends on the pixel itself, that is, the larger the pixel value, the greater the penalty. However, the blur reduces the high-frequency components of the clear image, and minimizing the high-frequency part will result in a blur solution or delta

Recently, data hiding and encryption methods have been very important for transmitting information to the relevant people correctly and reliably. Although each newly developed computer-aided data hiding and encryption method is stronger than previous methods, there may be security gaps or speed factors in future use. Therefore, faster and more secure encryption methods are needed. Chaotic systems can

Image inpainting attempts to fill the missing areas of an image with plausible content that is visually coherent with the image context. Semantic image inpainting has remained a challenging task even with the emergence of deep learning-based approaches. We propose a deep semantic inpainting model built upon a generative adversarial network and a dense U-Net network. Such a design helps achieve feature

The most common method for video-level classification is to operate models over a whole series of fixed-temporal-length clips in a long video. However, a video usually consists of several meaningless sections, and similar clips that are fed into models could lead to a suboptimal recognition accuracy and waste of computing resources. To address this issue, we introduced an adaptive discriminative clip

Intensity inhomogeneity, which is also called bias field, is ubiquitous in digital images. The causes of intensity inhomogeneity are complex and include uneven illumination and defects of imaging equipment. For images with local intensity inhomogeneity, an array of existing segmentation algorithms has poor performance on efficiency, accuracy, or initial robustness. To tackle this problem, an active

Recently, the attention mechanism and part-based architectures have greatly boosted the research of person re-identification (Re-ID). However, most attention-based works extract first-order information and lack diversity. Meanwhile, the classic part-based works are not able to make use of cross-part information because of the unified partitions. These two kinds of methods ignore visual relationships

Neural networks are vulnerable to various adversarial perturbations added to the input. Highly sparse adversarial perturbations are difficult to identify, which is especially dangerous to network security. Previous research has shown that ℓ0-norm has good sparsity but is challenging to solve. We use ℓq-norm to approach ℓ0-norm and propose a new white-box algorithm to generate adversarial examples aiming

Over the past few decades, we have witnessed a large family of algorithms that has been designed to provide different solutions to the problem of dimensionality reduction (DR). DR is an essential tool to excavate important information from the high-dimensional data by mapping the data to a low-dimensional subspace. Even though most DR algorithms can achieve satisfactory performance for many practical

Deep convolutional neural networks (CNNs) have achieved considerable success in terms of image denoising. However, previous CNN denoisers have been restricted by rigid kernel convolution that applies equal spatial treatment across images. To fully utilize the local differences, we propose a kernel prediction network that examines each pixel region and predicts unique pixel-wise kernels. Several optimizations

Underwater fish recognition is an important task in fish stock assessment and marine ecosystem studies. Machine learning techniques have been applied to train high-performance fish recognition models from underwater images. However, underwater images often contain extremely noisy backgrounds, hindering the training of accurate recognition models. Traditional methods exploit handcrafted features to

Self-supervised learning of depth from monocular videos has recently drawn attention as it has notable advantages over supervised ones in a training framework. We propose a self-supervised monocular depth estimation method with a set of improvements, which together result in both quantitatively and qualitatively improved depth maps compared to competing self-supervised methods. Our architecture amends

Near-infrared (NIR) face recognition (FR) has demonstrated robustness against changes in ambient illumination, which makes it suitable for surveillance even under weak illumination conditions. However, the existing database for NIR FR only contains frontal face images, and the impact of pose variation on the robustness of NIR FR remains unascertained. We developed an NIR face database with 57 pose

A deep learning framework is proposed to recognize large-scale plant species by integrating attention-based deep feature extraction network and plant taxonomy-guided path-based tree classifier. First, a plant taxonomy is constructed for organizing large-scale fine-grained plant species hierarchically in a coarse-to-fine fashion. Second, a deep learning framework is proposed, where attention mechanism

We present an image inpainting technique using frequency-domain information. Prior works on image inpainting predict the missing pixels by training neural networks using only the spatial-domain information. However, these methods still struggle to reconstruct high-frequency details for real complex scenes, leading to a discrepancy in color, boundary artifacts, distorted patterns, and blurry textures

Recently, anchor-free methods have brought new ideas to the field of object detection that eliminate the need for anchor boxes in object detection and provide a simpler detection structure. CenterNet is the representative anchor-free method. However, this method still has the problem of obtaining high-resolution representation from low-resolution representation using upsampling, and the predicted heatmap

Few-shot learning aims to learn classification with only a few training examples per class. The metric-based approaches aim to learn a set of embedding functions, so that when represented in this embedding, images are easy to recognize. For metric-based few-shot learning, how to get the class feature representation under a few support samples and what metric to use are important. We propose a multiclass

The denoising of interferometric phase images attracts many researchers. Natural image denoising algorithms based on neural networks are often proposed in the development of deep learning methods. A neural network, In-CNN, is derived from an advanced natural image denoising network and proposed for interferometric phase image denoising. Preactivation and residual learning methods are combined and applied

Currently, there is an increasing demand for wooden furniture and products, especially in the decorating business where wood textures are widely used. These textures have different artificial designs, and because different consumers have different needs for wood texture, the trend of using computer algorithms to design wood textures emerged. We propose a method for synthesizing wood’s heterogeneous

Underwater images suffer from haze, color distortion, and low contrast due to the absorption and scattering of transmitted light. A systematic approach including accurate background light (BL) estimation and transmission (TM) estimation is proposed based on color-line model for underwater images restoration. The estimation of BL candidate region is improved by combining quadtree subdivision and minimum

Image retrieval based on deep learning of hash has made great progress. The hash method increases retrieval speed greatly while saving storage space. However, some problems exist, such as the distinctiveness of image feature still needs to be improved and some image features are still redundant. We propose a new deep learning to hash method, namely, deep multiscale divergence hashing, which provides

Rapid and accurate detection of crowd abnormal events, such as stampedes and violent attacks in public places, has great research significance and application value. Due to the diversity and uncertainty of abnormal events, almost all existing methods tackle the problem by minimizing the reconstruction errors of training data, which cannot guarantee a larger reconstruction error for all abnormal events

Sparse prior to image denoising is a classical research field with a long history in computer vision. We propose an end-to-end supervised neural network, named DnMLCSC-net, which is inspired via multi-layer convolutional sparse coding model embedded with symbiotic analysis–synthesis priors for natural image denoising. Unfolding a multi-layer, learned iterative soft thresholding algorithm (ML-LISTA)

3D object recognition from point clouds is a fast-growing field of research in computer vision. 3D object recognition methods can be classified into two categories: global feature-based and local feature-based methods. The local feature-based methods are more popular than global ones. Because the global feature-based methods need a prior segmentation of the scene, they are not suitable for real-world

This guest editorial summarizes the Special Section on Perceptually Optimized Imaging.

Video saliency detection, which suffers from the interference of complicated motion and complex background, aims at discovering the motion-related and the most noticeable object in a video sequence while maintaining the spatiotemporal consistency of saliency maps. We propose a video saliency detection approach by constructing the moving object properties, meaning that motion is in concurrence with

Robust steganography enables secret information to be transmitted stealthily and accurately in lossy channels such as social channels and wireless channels. With the development of deep learning, robust steganography can be constructed using the generative model of deep neural networks. Two new robust steganographic frameworks are proposed on the basis of generative models, and two algorithms are proposed

A multi-secret image sharing (MSIS) scheme is a procedure to break down secret images into numerous shares and distribute each share to each authorized participant. Most existing ( n , n ) schemes face the problem of all-or-nothing that is all the n shares are required to reconstruct a secret image. If a single share is lost, then the secret image will not be recovered. Moreover, the existing ( k

The new video coding standard, known as versatile video coding (VVC) is projected to be concluded by the end of 2020. This standard is conducted mainly to address 8k videos and emerging applications such as 360 deg and high dynamic range. Intraprediction is a part of the prediction step in the video coding that exploits spatial redundancy. This module has been improved, compared to the high-efficiency

Face aging and rejuvenating work effectively in public security criminal investigation, cross-age recognition, and entertainment. However, three main problems still exist: the lack of accurate and sufficient dataset, low aging effect, and poor preservation of personal information. We propose a semi-supervised face aging and rejuvenating method for face aging and rejuvenating. In particular, a conditional

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