Adaptive edge-preserving image denoising using wavelet transforms

Image edge detection based on singular value feature vector and gradient operator

This paper presents an edge detection algorithm based on singular value eigenvector and gradient operator. In the proposed algorithm, the singular values of image blocks are first calculated, and the Sobel gradient template is extended to eight other directions. Then the gradient values of image pixels are determined according to the stability of the singular values of image blocks. The determination of gradient threshold is considered from both global and local aspects. After calculating the global and local gradient thresholds of the original image, the gradient threshold of the whole image is determined by weighting function. Then the edge pixels of the image are filtered according to the gradient threshold, and the edge information image of the original image is obtained. The experimental data show that the proposed algorithm can resist a certain degree of noise interference, and the accuracy and efficiency of edge extraction are better than other similar algorithms.

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.

Study of an image autofocus method based on power threshold function wavelet reconstruction and a quality evaluation algorithm

As a key component in optical microscopy imaging systems, autofocus technology has a significant effect on imaging quality. In this paper, an optical microscopy autofocus method that includes a wavelet denoising algorithm based on a power threshold function and a Brenner image quality evaluation algorithm is presented. Experimental results show that the power threshold function wavelet denoising algorithm, which can be adopted to obtain more realistic optical images, is superior to the traditional soft, hard, hyperbolic, and exponential threshold functions in terms of peak signal-to-noise ratio, signal-to-noise ratio, mean squared error, and histogram indicators; moreover, compared to the Roberts, sum modulus difference (SMD), and energy gradient functions, the Brenner image quality evaluation algorithm can be used to quickly and accurately lock onto the focal plane. By integrating and applying these two core algorithms in the autofocus image acquisition system of a microscope, the image sharpness and focusing quality are greatly improved, which benefits the further evaluation of images.

Local Sparse Structure Denoising for Low-Light-Level Image

Sparse and redundant representations perform well in image denoising. However, sparsity-based methods fail to denoise low-light-level (LLL) images because of heavy and complex noise. They consider sparsity on image patches independently and tend to lose the texture structures. To suppress noises and maintain textures simultaneously, it is necessary to embed noise invariant features into the sparse decomposition process. We, therefore, used a local structure preserving sparse coding (LSPSc) formulation to explore the local sparse structures (both the sparsity and local structure) in image. It was found that, with the introduction of spatial local structure constraint into the general sparse coding algorithm, LSPSc could improve the robustness of sparse representation for patches in serious noise. We further used a kernel LSPSc (K-LSPSc) formulation, which extends LSPSc into the kernel space to weaken the influence of linear structure constraint in nonlinear data. Based on the robust LSPSc and K-LSPSc algorithms, we constructed a local sparse structure denoising (LSSD) model for LLL images, which was demonstrated to give high performance in the natural LLL images denoising, indicating that both the LSPSc- and K-LSPSc-based LSSD models have the stable property of noise inhibition and texture details preservation.