MODIFIED MODEL OF IMAGE NOISE FILTRATION BASED ON CONVOLUTIONAL NEURAL NETWORK

Abstract

In recent times, there has been significant progress in the application of deep learning, particularly using convolutional neural networks (CNNs), to address image denoising tasks, driven by their exceptional performance in image processing. However, it is noteworthy that CNNs are predominantly used for dealing with Gaussian noise, and there is currently limited work on effectively reducing salt-and-pepper (SAP) noise. One of the strategies to tackle the issue of noisy images is the development of efficient deep learning models specialized in handling SAP noise. This paper explores the potential enhancement of the proposed SeConvNet model, designed specifically for reducing noisy images contaminated with SAP noise. Salt-and-pepper noise, characterized by random occurrence of black and white pixels, is a common type of noise found in images. Given the current relevance of noise reduction in images, particularly in the absence of sufficient models dedicated to SAP noise, this paper introduces a block aimed at potentially improving the performance of the existing model. The results of this study demonstrate the promise of advancing the direction of enhancing the original model, which in turn could be beneficial for a wide range of applications, including medical diagnostics and any domain where image processing is crucial for precise outcomes. Implementing the proposed improvements could have a positive impact on processing images with SAP noise, considering the scarcity of models addressing this specific problem. The model was trained on the well-known BSD68 dataset, ensuring an objective evaluation of the results. Additionally, the paper presents an analysis of existing models that target SAP noise reduction, providing insights into the current landscape of techniques in this domain.