Brief review of image denoising techniques

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.

SHAPE PRESERVING QUADRATIC SPLINEINTERPOLATION

A new method for constructing quadratic spline to interpolate a given sat of data points ispresented. The constructed spline preserves the shape of the given data points such as monotonicityand convexity , and is visually pleasing. Numerical experiments are included which compare the ″visu-ally pleasing″ and the approximation accuracy of the new method with other two methods.

CF-DAN: Facial-expression recognition based on cross-fusion dual-attention network

Recently, facial-expression recognition (FER)has primarily focused on images in the wild, includingfactors such as face occlusion and image blurring, ratherthan laboratory images. Complex field environmentshave introduced new challenges to FER. To addressthese challenges, this study proposes a cross-fusion dualattention network. The network comprises three parts:(1) a cross-fusion grouped dual-attention mechanism torefine local features and obtain global information;(2) aproposed C2 activation function construction method,which is a piecewise cubic polynomial with threedegrees of freedom, requiring less computation withimproved flexibility and recognition abilities, whichcan better address slow running speeds and neuroninactivation problems;and (3) a closed-loop operationbetween the self-attention distillation process andresidual connections to suppress redundant informationand improve the generalization ability of the model.The recognition accuracies on the RAF-DB, FERPlus,and AffectNet datasets were 92.78%, 92.02%, and63.58%, respectively. Experiments show that this modelcan provide more effective solutions for FER tasks.

Improved fuzzy clustering for image segmentation based on a low-rank prior

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.

Kernel-blending connection approximated by a neural network for image classification

This paper proposes a kernel-blending connection approximated by a neural network(KBNN)for image classification.A kernel mapping connection structure,guaranteed by the function approximation theorem,is devised to blend feature extraction and feature classification through neural network learning.First,a feature extractor learns features from the raw images.Next,an automatically constructed kernel mapping connection maps the feature vectors into a feature space.Finally,a linear classifier is used as an output layer of the neural network to provide classification results.Furthermore,a novel loss function involving a cross-entropy loss and a hinge loss is proposed to improve the generalizability of the neural network.Experimental results on three well-known image datasets illustrate that the proposed method has good classification accuracy and generalizability.

High-resolution images based on directional fusion of gradient

This paper proposes a novel method for image magnification by exploiting the property that the intensity of an image varies along the direction of the gradient very quickly. It aims to maintain sharp edges and clear details. The proposed method first calculates the gradient of the low-resolution image by fitting a surface with quadratic polynomial precision. Then,bicubic interpolation is used to obtain initial gradients of the high-resolution(HR) image. The initial gradients are readjusted to find the constrained gradients of the HR image, according to spatial correlations between gradients within a local window. To generate an HR image with high precision, a linear surface weighted by the projection length in the gradient direction is constructed. Each pixel in the HR image is determined by the linear surface. Experimental results demonstrate that our method visually improves the quality of the magnified image. It particularly avoids making jagged edges and bluring during magnification.

A nonlocal gradient concentration method for image smoothing

It is challenging to consistently smooth natural images, yet smoothing results determine the quality of a broad range of applications in computer vision. To achieve consistent smoothing, we propose a novel optimization model making use of the redundancy of natural images, by defining a nonlocal concentration regularization term on the gradient. This nonlocal constraint is carefully combined with a gradientsparsity constraint, allowing details throughout the whole image to be removed automatically in a datadriven manner. As variations in gradient between similar patches can be suppressed effectively, the new model has excellent edge preserving, detail removal,and visual consistency properties. Comparisons with state-of-the-art smoothing methods demonstrate the effectiveness of the new method. Several applications,including edge manipulation, image abstraction,detail magnification, and image resizing, show the applicability of the new method.

Image smoothing based on global sparsity decomposition and a variable parameter

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.

Salt and pepper noise removal in surveillance video based on low-rank matrix recovery

This paper proposes a new algorithm based on low-rank matrix recovery to remove salt &pepper noise from surveillance video. Unlike single image denoising techniques, noise removal from video sequences aims to utilize both temporal and spatial information. By grouping neighboring frames based on similarities of the whole images in the temporal domain, we formulate the problem of removing salt &pepper noise from a video tracking sequence as a lowrank matrix recovery problem. The resulting nuclear norm and L1-norm related minimization problems can be efficiently solved by many recently developed methods. To determine the low-rank matrix, we use an averaging method based on other similar images. Our method can not only remove noise but also preserve edges and details. The performance of our proposed approach compares favorably to that of existing algorithms and gives better PSNR and SSIM results.