By: Arti kumari and Mayank Pathak
1- Department of Computer Science and Engineering, Technocrats Institute of Technology, Bhopal, M.P
2- Professor, Department of Computer Science and Engineering, Technocrats Institute of Technology, Bhopal, M.P
Abstract: Clear and high-quality images have become more and more necessary in recent years due to the rapid development of computer vision-based autonomous systems, including autonomous driving, underwater robots, video surveillance, and medical imaging. However, real-world circumstances, such as inclement weather, underwater scenes, and moving cameras, can lead to poorer-quality photographs. These degradations, which include color distortions, noise, and blurring, hurt how well visual systems accomplish tasks like target tracking, segmentation, and detection. Image restoration (IR) methods have proven essential to addressing these issues. Convolutional neural networks (CNNs) and generative adversarial networks (GANs), in particular, are two types of deep learning models that have shown promise as effective tools for improving image quality. This study explores advancements in edge preservation, denoising, and deblurring while examining a range of deep-learning techniques applied to image restoration… The study emphasizes how crucial it is to create effective IR methods to increase visual systems’ capacity to adapt to complicated situations. In sectors including digital photography, medical imaging, and satellite data analysis, restoration of fine details in images has been greatly enhanced by the combination of classical and deep learning-based methods.
Keywords: Image Restoration, Deep Learning, Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), IR
Citation:
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