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.

Recently, facial-expression recognition (FER) has primarily focused on images in the wild, including factors such as face occlusion and image blurring, rather than laboratory images. Complex field environments have introduced new challenges to FER. To address these challenges, this study proposes a cross-fusion dual-attention network. The network comprises three parts: (1) a cross-fusion grouped dual-attention mechanism to refine local features and obtain global information; (2) a proposed C² activation function construction method, which is a piecewise cubic polynomial with three degrees of freedom, requiring less computation with improved flexibility and recognition abilities, which can better address slow running speeds and neuron inactivation problems; and (3) a closed-loop operation between the self-attention distillation process and residual connections to suppress redundant information and improve the generalization ability of the model. The recognition accuracies on the RAF-DB, FERPlus, and AffectNet datasets were 92.78%, 92.02%, and 63.58%, respectively. Experiments show that this model can provide more effective solutions for FER tasks.

Video super-resolution techniques aim to obtain high-resolution equivalents of existing low-resolution videos through a series of operations. In recent research, transformers have been increasingly popular because of their remarkable abilities in parallel computing and efficient extraction of space-time sequence features from videos. Moreover, combining self-attention and multi-scale methods has yielded excellent results. However, the combination of the two methods has limitations, current up-sampling methods struggle to match the global modeling capacity of self-attention mechanisms. Therefore, this paper proposes three strategies to combine the two methods. Based on the approximation strategy, we first construct a new bilinear up-sampling method for multi-scale acquisition. Convolution and cross-attention techniques are then used to correct and align features at different scales to prevent large deviations in feature extraction at a specific scale, which can affect subsequent feature extraction. Finally, to effectively solve the common computational complexity, C ⁰ continuity, and neuron death problems of existing activation functions, a new method to construct the activation function is proposed. The cubic spline function is used to construct a new activation function approximating tanh. The new activation function is C ² continuous, which is piecewise defined by cubic polynomial curves. In this study, better results were achieved on three public video super-resolution test sets: REDS4, Vid4, and Vimeo-90K-T. Experiments demonstrated that the proposed method could provide a new solution for video super-resolution tasks.

Personal credit scoring is a challenging issue. In recent years, research has shown that machine learning has satisfactory performance in credit scoring. Because of the advantages of feature combination and feature selection, decision trees can match credit data which have high dimension and a complex correlation. Decision trees tend to overfitting yet. eXtreme Gradient Boosting is an advanced gradient enhanced tree that overcomes its shortcomings by integrating tree models. The structure of the model is determined by hyperparameters, which is aimed at the time-consuming and laborious problem of manual tuning, and the optimization method is employed for tuning. As particle swarm optimization describes the particle state and its motion law as continuous real numbers, the hyperparameter applicable to eXtreme Gradient Boosting can find its optimal value in the continuous search space. However, classical particle swarm optimization tends to fall into local optima. To solve this problem, this paper proposes an eXtreme Gradient Boosting credit scoring model that is based on adaptive particle swarm optimization. The swarm split, which is based on the clustering idea and two kinds of learning strategies, is employed to guide the particles to improve the diversity of the subswarms, in order to prevent the algorithm from falling into a local optimum. In the experiment, several traditional machine learning algorithms and popular ensemble learning classifiers, as well as four hyperparameter optimization methods (grid search, random search, tree-structured Parzen estimator, and particle swarm optimization), are considered for comparison. Experiments were performed with four credit datasets and seven KEEL benchmark datasets over five popular evaluation measures: accuracy, error rate (type I error and type II error), Brier score, and score. Results demonstrate that the proposed model outperforms other models on average. Moreover, adaptive particle swarm optimization performs better than the other hyperparameter optimization strategies.