A Novel Approach of K-SVD-Based Algorithm for Image Denoising

Abstract

In recent years, denoising has played an important role in medical image analysis. Image denoising is still accepted as a challenge for researchers and image application developers in medical image applications. The idea is to denoise a microscopic image through over-complete dictionary learning using a k-means algorithm and singular value decomposition (K-SVD) based on pursuit methods. This approach is good in performance on the quality improvement of the medical images, but it has low computational speed with high computational complexity. In view of the above limitations, this chapter proposes a novel strategy for denoising insight phenomena of the K-SVD algorithm. In addition, the authors utilize the technology of improved dictionary learning of the image patches using heap sort mechanism followed by dictionary updating process. The experimental results validate that the proposed approach successfully reduced noise levels on various test image datasets. This has been found to be more accurate than the best in class denoising approaches.