Enhancing Image Processing with CNNs: A Comparative Analysis with other Research and Current Sources

Abstract

The development of convolutional neural networks (CNNs) has been a major factor in the tremendous progress made in the field of image processing in recent decades. CNNs were first created for simple image identification tasks, but they have now matured into sophisticated models that can handle challenging computer vision problems. By contrasting the efficacy of these contemporary neural network architectures with more conventional approaches reported in earlier research, this essay aims to investigate the revolutionary influence of CNNs on image processing techniques. The conversation will emphasize the critical role CNNs play in improving accuracy, efficiency, and utility in image processing applications by examining both historical methods and contemporary advancements. This analysis will also take into account the effects of these developments on a number of fields, such as digital media, driverless cars, and medical imaging.