Digital Image Denoising Techniques Based on Multi-Resolution Wavelet Domain with Spatial Filters: A Review

Abstract

Recently, with the explosion in the number of digital images captured every day in all life aspects, there is a growing demand for more detailed and visually attractive images. However, the images taken by current sensors are inevitably degraded by noise in various fields, such as medical, astrophysics, weather forecasting, etc., which contributes to impaired visual image quality. Therefore, work is needed to reduce noise by preserving the textural, information, and structural features of the image. So far, there are different techniques for reducing noise that various researchers have done. Each technique has its advantages and disadvantages. In this paper, a review of some significant work in the field of image denoising based on that the denoising methods is presented. These methods can be classified as wavelet domain, spatial domain, or both methods can combine to obtain the advantage them. After a brief discussion, the classification of image denoising techniques is explained. A comparative analysis of various image denoising methods is also performed to help researchers in the image denoising area. Besides, standard measurement parameters have been used to compute the results and to evaluate the performance of the used denoising techniques. This review paper aims to provide functional knowledge of image denoising methods in a nutshell for applications using images to provide ease for selecting the ideal strategy according to the necessity.

Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters

“…techniques [7,20] In image processing, there are several denoising methods. The choice of the denoising technique used in digital images depends on the type of noise present in degraded images and also levels of noise ratios.…”

“…The choice of the denoising technique used in digital images depends on the type of noise present in degraded images and also levels of noise ratios. According to the noise source, it can be roughly divided into two categories: external/internal noise [7]. The fundamental methods of image denoising can be divided into two major approaches: spatial domain filtering and wavelet domain methods, as presented in Figure 1.…”

“…The WT technique is advantageous for energy compression since it generates small and large coefficients as a result of noise and key image elements. Thresholding the low coefficients is doable without compromising the image's significant features [7].…”

“…SWT implements a tree-structured algorithm similar to DWT, but it achieves and returns a better approximation result than DWT because the output signal is not decimated (without down-sampling), which DWT cannot achieve. As a result, a multi-layer SWT applies to overcome the limitation of the conventional wavelet transform [7].…”

An Improved BM3D-Canny-Zernike Algorithm for Micro-Size Detection of Electronic Connectors

“…Secondly, it is difficult to extract subpixel edge features with sufficient accuracy. The existing studies on image denoising and edge detection mainly employ two kinds of methods: traditional methods [10][11][12][13][14][15][16][17], and deep learning-based methods [18][19][20][21][22][23][24][25]. With a fixed threshold, the traditional methods cannot adapt to the image scene with flexible noise and edge changes.…”