2020/10/14 by Kelum Gajamannage, Gajamannage, Kelum · 2 citations
Computer Science · #68T10 #68U10 #94A08 #Advanced Vision and Imaging #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Contourlet #FOS: Computer and information sciences #FOS: Electrical engineering #I.4.3 #I.4.5 #Image (mathematics) #Image and Signal Denoising Methods #Image and Video Processing (eess.IV) #Image denoising #Image processing #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #Noise (video) #Noise reduction #Non-local means #Pattern recognition (psychology) #Pixel #Video denoising #Video processing #Wavelet #Wavelet transform #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2010.07769
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2020/10/14 · openalex created_date 2022/07/25 · openalex updated_date 2026/08/05
With the proliferation of sophisticated cameras in modern society, the demand for accurate and visually pleasing images is increasing. However, the quality of an image captured by a camera may be degraded by noise. Thus, some processing of images is required to filter out the noise without losing vital image features. Even though the current literature offers a variety of denoising methods, the fidelity and efficacy of their denoising are sometimes uncertain. Thus, here we propose a novel and computationally efficient image denoising method that is capable of producing accurate images. To preserve image smoothness, this method inputs patches partitioned from the image rather than pixels. Then, it performs denoising on the manifold underlying the patch-space rather than that in the image domain to better preserve the features across the whole image. We validate the performance of this method against benchmark image processing methods.