• SACHIN GUPTA
• Preetam
• Anuj Soni
• Anant Preet Singh
• Shafalii Sharma

This research paper explores the realm of super-resolution (SR) in the context of computer vision, with a particular focus on deep convolutional neural networks (CNNs) and sparse coding techniques. The fundamental goal is to generate high-resolution images from their low-resolution counterparts, significantly improving visual quality. We delve into state-of-the-art techniques, architectural designs, and training strategies that have advanced the field. Furthermore, we address challenges and limitations, including computational complexity, domain generalization, and real-world applicability. This paper lays the foundation for understanding the principles, recent advancements, and future directions in the pursuit of enhanced visual quality through SR techniques.

Super-resolution, convolutional neural network, sparse coding, image processing, visual quality enhancement

"Revolutionizing Image Enhancement - A Comprehensive Study of Super-Resolution Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.e14-e18, November-2023, Available :http://www.jetir.org/papers/JETIR2311403.pdf

"Revolutionizing Image Enhancement - A Comprehensive Study of Super-Resolution Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppe14-e18, November-2023, Available at : http://www.jetir.org/papers/JETIR2311403.pdf