Review of Hybrid Denoising Approaches in Face Recognition

Bridging Wavelet Transform and Deep Learning

Abstract

Statistically, image denoising is one of the key pillars of image processing and picture acquisition, which also is utilized to clear the noisy images. Over the last years, there is an increase of study subjects that are devoting to designing and making noise cancellation methods. This study reviews all major image denoising techniques, with a special emphasis on integrated deep learning approaches as well as traditional signal processing methods. The review presents a broad array of techniques for instance convolutional neural networks (CNNs), wavelet transforms, hybrid models, and their emendations. The lecturer will focus on the advantages, as well as the disadvantages, of each methodology along with their appropriateness in various fields, from which the current state of the art image denoising can be concluded. On the other hand, the paper discusses critical barriers leading to further prospects of research in cybersecurity and cybercrime prevention This review is important in that it aims to serve researchers, practitioners, and enthusiasts who would like to peer into the new trends and developments in denoise image generation.