Locally Adaptive Bilateral Clustering for Image Denoisingand Sharpness Enhancement

Abstract

The purpose of denoising is to remove the noisewhile retaining the edges and other detailed features as much aspossible. This paper, present a method for both image denoisingand sharpness enhancement. We approach the problems ofdenoising and sharpening by first adaptively segmenting theimage into clusters based on features that represent theunderlying local image structures (e.g., image details, edges,and textures). The key idea behind this approach is denoisingand sharpening according to the local image feature, so thatnoise amplification, undershoots, and overshoots can beeffectively avoided. The parameters which are used to test theperformance of the proposed method are i) Peak Signal to NoiseRatio (PSNR) and ii) Mean Absolute Error (MAE). Furtherthe results which are obtained using the proposed method interm of PSNR and MAE were compared with the earliestpublished works. The results clearly confirm the superiority ofour proposed method for sharpness enhancement and imagedenoising

Enhancement of laser noise image based on photorefractive nonlinearity of lithium niobate crystals

“…To enhance the accuracy of information retrieval from images, researchers have developed various processing methods. When the noise intensity is lower than the image signal intensity, the noise can be eliminated as a perturbation of the original image data using image-processing algorithms based on filtering technology [1][2][3][4] or wavelet analysis [5][6][7][8][9][10] . Wavelet analysis, known for its good localization properties in both time and frequency domains, has made it the most effective among various image de-noising techniques.…”