Abstract
Image denoising is a major tricky in image processing. The main determination is to quash noise from the degraded image while keeping other details of the image unchanged. In recent years, many multi-resolution based approaches have attained great success in image denoising. In a nut shell, the wavelet transform provide an optimal representation of a noisy image, including a signal with information from a limited number of coefficients and noise by all other left over coefficients. The most popular way to eliminate noise is to threshold the noise affected wavelet coefficient. The noise affected wavelet coefficient shrinkage is better, only if the threshold value is properly selected. Therefore, the performance of various wavelet based denoising techniques depends on the estimation of the threshold value. Different techniques are available to find the threshold value. The aim of this study is to discuss denoising schemes based on various wavelet transforms using threshold approach. Hence, this article examines the research article with threshold selection based on spatial adaptivity, sub-band adaptivity and also hybrid methods with multi-resolution wavelet structures.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.