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With the explosion in the number of digital images taken every day, the demand for more accurate and visually pleasing images is increasing. However, the images captured by modern cameras are inevitably degraded by noise, which leads to deteriorated visual image quality. Therefore, work is required to reduce noise without losing image features (edges, corners, and other sharp structures). So far, researchers have already proposed various methods for decreasing noise. Each method has its own advantages and disadvantages. In this paper, we summarize some important research in the field of image denoising. First, we give the formulation of the image denoising problem, and then we present several image denoising techniques. In addition, we discuss the characteristics of these techniques. Finally, we provide several promising directions for future research.

The future 6G (sixth-generation) mobile communication technology is required to support advanced network services capabilities such as holographic communication, autonomous driving, and the industrial internet, which demand higher data rates, lower latency, and greater reliability. Furthermore, future service classifications will become more fine-grained. To meet the requirements of these low-latency services with varying granularities, this work investigates fine-grained network slicing for low-latency services in 6G networks. A fine-grained network slicing algorithm for low-latency services in 6G based on GCNs (graph convolutional networks) is proposed. The goal is to minimize the end-to-end delay of network slicing while meeting the constraints of computational resources, communication resources, and the deployment of SFCs (service function chains). This algorithm focuses on the construction and deployment of network slices. First, due to the complexity and diversity of 6G networks, DAGs (Directed Acyclic Graphs) are used to represent network service requests. Then, based on the depth-first search algorithm, three types of SFCs of latency-type network slices are constructed according to the available computing and communication resources. Finally, the GCN-based low-latency service fine-grained network slicing algorithm is used to deploy SFCs. The simulation results show that the latency performance of the proposed algorithm outperforms that of the Double DQN and DQN algorithms across various scenarios, including changes in the number of underlying network nodes and variations in service sizes.

The models based on convolutional neural network have achieved excellent results in image super-resolution by acquiring prior knowledge from a large number of images, but such models still have problems such as the features between layers in the depth network cannot be effectively fused, the number of parameters is too large, and cross-channel feature learning is impossible. Based on this, a deep recursive residual channel attention network (DRRCAN) model was proposed in this paper. To solve the problem that the information between different layers in the deep network cannot be fused effectively, this paper constructs a channel feature fusion module, which can effectively fuse the feature information of different layers. To solve the problem that the parameters increase sharply due to the increase of network depth, recursive blocks are adopted in this paper, which greatly reduces the number of parameters in the deep network. The channel attention is integrated to enable the model to learn features across channels. In addition, to avoid gradient explosion or disappearance, residual modules, long skip connections are introduced to improve the stability and generalization ability of the model. Extensive benchmark evaluations validate the superiority of the proposed DRRCAN model compared with existing algorithms.

Promoting the spatial resolution of hyperspectral sensors is expected to improve computer vision tasks. However, due to the physical limitations of imaging sensors, the hyperspectral image is often of low spatial resolution. In this paper, we propose a new hyperspectral image super-resolution method from a low-resolution (LR) hyperspectral image and a high resolution (HR) multispectral image of the same scene. The reconstruction of HR hyperspectral image is formulated as a joint estimation of the hyperspectral dictionary and the sparse codes based on the spatial-spectral sparsity of the hyperspectral image. The hyperspectral dictionary is learned from the LR hyperspectral image. The sparse codes with respect to the learned dictionary are estimated from LR hyperspectral image and the corresponding HR multispectral image. To improve the accuracy, both spectral dictionary learning and sparse coefficients estimation exploit the spatial correlation of the HR hyperspectral image. Experiments show that the proposed method outperforms several state-of-art hyperspectral image super-resolution methods in objective quality metrics and visual performance.

Smoothing images, especially with rich texture, is an important problem in computer vision. Obtaining an ideal result is difficult due to complexity, irregularity, and anisotropicity of the texture. Besides, some properties are shared by the texture and the structure in an image. It is a hard compromise to retain structure and simultaneously remove texture. To create an ideal algorithm for image smoothing, we face three problems. For images with rich textures, the smoothing effect should be enhanced. We should overcome inconsistency of smoothing results in different parts of the image. It is necessary to create a method to evaluate the smoothing effect. We apply texture pre-removal based on global sparse decomposition with a variable smoothing parameter to solve the first two problems. A parametric surface constructed by an improved Bessel method is used to determine the smoothing parameter. Three evaluation measures: edge integrity rate, texture removal rate, and gradient value distribution are proposed to cope with the third problem. We use the alternating direction method of multipliers to complete the whole algorithm and obtain the results. Experiments show that our algorithm is better than existing algorithms both visually and quantitatively. We also demonstrate our method’s ability in other applications such as clip-art compression artifact removal and content-aware image manipulation.

Image segmentation is a basic problem in medical image analysis and useful for disease diagnosis. However, the complexity of medical images makes image segmentation difficult. In recent decades, fuzzy clustering algorithms have been preferred due to their simplicity and efficiency. However, they are sensitive to noise. To solve this problem, many algorithms using non-local information have been proposed, which perform well but are inefficient. This paper proposes an improved fuzzy clustering algorithm utilizing nonlocal self-similarity and a low-rank prior for image segmentation. Firstly, cluster centers are initialized based on peak detection. Then, a pixel correlation model between corresponding pixels is constructed, and similar pixel sets are retrieved. To improve efficiency and robustness, the proposed algorithm uses a novel objective function combining non-local information and a low-rank prior. Experiments on synthetic images and medical images illustrate that the algorithm can improve efficiency greatly while achieving satisfactory results.

The paper aims to investigate social commerce systems from a systems thinking perspective. It proposes to model the social commerce process and outlines how Following, Communicating, Purchasing, and Sharing are systematically connected with each other in the social commerce process. The paper describes an exploratory review study using the systematic literature review method, including 384 social commerce research papers, which were published from 2011 to 2021. The data are refined by documentary analysis, including Study Selection Criteria and Quality Assessment processes. The paper systematically develops a conceptual framework for understanding social commerce. Previous research on social commerce mainly focuses on one or more particular key success factors (such as trust) in social commerce, and a few of them investigate social commerce as an integral business system. This review provides a more comprehensive basis for future social commerce research.

Stock constitutes a crucial element of the financial market, and accurately forecasting stock trends remains a significant and unresolved issue. Nonetheless, the stock’s considerable complexity renders accurate prediction of stock trends more challenging. This paper proposes a novel multi-perspective approach that converts the time series prediction challenge into an image classification problem, referred to as the Multi-perspective Denoise Transformer (MPDTransformer). We initially multi-factor features into two-dimensional images employing a multi-perspective approach to more comprehensively explain the actual market conditions and enhance the model’s practicality and adaptability; secondly, we utilize a Convolutional Autoencoder (CAE) to extract features, which effectively eliminates noise and enhances data purity; finally, to comprehensively capture the temporal relationships within the data and gain a deeper understanding of the overall time series, we employ a Transformer for prediction. Experimental results demonstrate that our method outperforms other prevalent stock trend prediction techniques.

To handle the task of pointer meter reading recognition, in this paper, we propose a deep network model that can accurately detect the pointer meter dial and segment the pointer as well as the reference points from the located meter dial. Specifically, our proposed model is composed of three stages: meter dial location, reference point segmentation, and dial number reading recognition. In the first stage, we translate the task of meter dial location into a regression task, which aims to separate bounding boxes by an object detection network. This results in the accurate and fast detection of meter dials. In the second stage, the dial region image determined by the bounding box is further processed by using a deep semantic segmentation network. After that, the segmented output is used to calculate the relative position between the pointer and reference points in the third stage, which results in the final output of reading recognition. Some experiments were conducted on our collected dataset, and the experimental results show the effectiveness of our method, with a lower computational burden compared to some existing works.

Image registration aims to establish an active correspondence between a pair of images. Such correspondence is critical for many significant applications, such as image fusion, tumor growth monitoring, and atlas generation. In this study, we propose an unsupervised deformable image registration network (UDIR-Net) for 3D medical images. The proposed UDIR-Net is designed in an encoder-decoder architecture and directly estimates the complex deformation field between input pairwise images without any supervised information. In particular, we recalibrate the feature slice of each feature map that is propagated between the encoder and the decoder in accordance with the importance of each feature slice and the correlation between feature slices. This method enhances the representational power of feature maps. To achieve efficient and robust training, we design a novel hierarchical loss function that evaluates multiscale similarity loss between registered image pairs. The proposed UDIR-Net is tested on different public magnetic resonance image datasets of the human brain. Experimental results show that UDIR-Net exhibits competitive performance against several state-of-the-art methods.