Open AccessA Review on Image Denoising Techniques
Author(s) -
Smita Vasant Tempe
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.39342
Subject(s) - discretization , domain (mathematical analysis) , curvelet , noise reduction , filter (signal processing) , computer science , wavelet , image (mathematics) , upsampling , algorithm , artificial intelligence , computer vision , convergence (economics) , discrete wavelet transform , image denoising , mathematics , wavelet transform , mathematical analysis , economics , economic growth
The goal of this study is to find a "genuine" two-dimensional transform that can capture the fundamental geometrical structure that is important in visual information. The discontinuous character of the data is the most difficult aspect of analysing geometry in photographs. Unlike previous approaches, such as curvelets, which generate a transform in the continuous domain and then discretize for sampled data, we begin with a discrete-domain construction and then investigate its convergence to a continuous-domain expansion. We use nonseparable filter banks to create a discrete-domain multiresolution and multidirection expansion, similar to how wavelets are produced from filter banks. As a result of this construction, a flexible multiresolution, local, and directed picture expansion employing contour segments is obtained, and it is therefore useful.