Exploring Advanced Deep Learning Methods for Enhancing Image Clarity: A Review

The field of image denoising has seen a significant shift towards deep learning methodologies in recent years. Various approaches using deep learning have been developed for denoising images, including discriminative methods which have proven effective in addressing issues related to Gaussian noise. Similarly, learning-based methods have demonstrated their capability in accurately estimating noise. Despite these notable advancements, there remains a lack of comprehensive research that effectively summarizes the various deep learning techniques available for image denoising. To address this gap, this paper undertakes a comparative analysis of different deep learning techniques in the context of image denoising. The study begins by categorizing deep convolutional neural networks into three distinct groups: those specifically designed for handling additive white Gaussian noise; those geared towards real noise; and those focused on blind denoising. Furthermore, the paper identifies several challenges and proposes suggestions for future research directions in deep-learning-based image denoising. These efforts aim to shed light on areas that necessitate further exploration and development, thereby paving the way for continued progress in this domain.