Feature-based wavelet shrinkage algorithm for image denoising

Abstract

Abstract-A selective wavelet shrinkage algorithm for digital image denoising is presented. The performance of this method is an improvement upon other methods proposed in the literature and is algorithmically simple for large computational savings. The improved performance and computational speed of the proposed wavelet shrinkage algorithm is presented and experimentally compared with established methods. The denoising method incorporated in the proposed algorithm involves a two-threshold validation process for real-time selection of wavelet coefficients. The two-threshold criteria selects wavelet coefficients based on their absolute value, spatial regularity, and regularity across multiresolution scales. The proposed algorithm takes image features into consideration in the selection process. Statistically, most images have regular features resulting in connected subband coefficients. Therefore, the resulting subbands of wavelet transformed images in large part do not contain isolated coefficients. In the proposed algorithm, coefficients are selected due to their magnitude, and only a subset of those selected coefficients which exhibit a spatially regular behavior remain for image reconstruction. Therefore, two thresholds are used in the coefficient selection process. The first threshold is used to distinguish coefficients of large magnitude and the second is used to distinguish coefficients of spatial regularity. The performance of the proposed wavelet denoising technique is an improvement upon several other established wavelet denoising techniques, as well as being computationally efficient to facilitate real-time image-processing applications.

Wavelet Based Joint Denoising of Depth and Luminance Images

“…Although depth images can be observed as ordinary images and denoised using some of the numerous image or video denoising algorithms, such as [1], [2] and [3] better denoising results can be obtained by jointly using of luminance and depth information, because of the interdependencies between them.…”

An Efficient Preprocessing Technique for Noise Reduction in Ear Verification System

“…In this section, first of all, the performance of proposed method is evaluated by some gray level test images as visual including Sail, Baboon, Peppers, and Lena in comparison with Median Filter (MF) [28], Iterative Median Filter (IMF) [29], Fuzzy Filter (FF) [30], and proposed method in figures of 5 to 8.…”

“…The proposed method at the first phase, ANFIS [12] identifies Noisy pixels with high accuracy, then FWS [27] is used based on the information of noisy and uncorrupted noisy pixels, so noisy pixel is replaced by new some value and unnoisy pixel remain naturally [28]. The main structure of proposed method for denoising is presented as figure 2.…”

A New Fuzzy-Based Wavelet Shrinkage Image Denoising Technique

“…-WAVELET: the bivariate wavelet shrinkage function proposed by Şendur [36], the feature-based wavelet shrinkage method proposed by Balster [37] and the probabilistic shrinkage function proposed by Pižurica [1].…”