Abstract

In computer vision, image super-resolution plays a pivotal role in improving the visual quality of low-resolution images, thereby enhancing various applications such as medical imaging, surveillance, and digital entertainment. The problem at hand involves the inherent limitations of conventional methods in restoring high-frequency information lost during image downscaling. This research aims to bridge this gap by leveraging DCNNs, exploiting their ability to learn complex mappings between low and high-resolution image spaces. This study addresses the challenge of image super-resolution through the application of Deep Convolutional Neural Networks (DCNNs). The research involves the design and training of a novel DCNN architecture tailored specifically for image super-resolution. We employ a large dataset of low and high-resolution image pairs to facilitate supervised learning. The network is trained to intelligently infer high-frequency details from low-resolution inputs, enabling the generation of visually compelling super-resolved images. Results from extensive experiments showcase the superior performance of the proposed DCNN-based approach compared to traditional methods. Quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSI), demonstrate significant improvements in image quality. Additionally, qualitative assessments highlight the network’s ability to reconstruct fine details, edges, and textures, resulting in visually pleasing super-resolved images.

Authors

Archana Tomar and Harish Patidar
Mandsaur University, India

Keywords

Deep Convolutional Neural Networks, Image Super-Resolution, Computer Vision, Neural Network Training, High-Resolution Imaging