EDSR: Empowering super-resolution algorithms with high-quality DIV2K images

Abstract

Remarkable strides have been made in Enhanced-resolution image reconstruction in recent times. In this research, we propose EDSR, a novel algorithm leveraging high-quality DIV2K images to enhance super-resolution algorithms’ performance. Our approach addresses challenges associated with capturing and reconstructing fine details and textures in low-resolution images. The training process utilizes a deep neural network fed with the extensive DIV2K dataset, comprising meticulously selected high-resolution images for super-resolution tasks. The network learns from these high-quality images to improve super-resolved outputs’ accuracy and fidelity. Extensive experimentation evaluates the effectiveness of the EDSR algorithm in achieving superior results. Experimental outcomes exhibit the remarkable progress made by EDSR compared to other leading super-resolution algorithms. The mean PSNR values obtained using EDSR on various test datasets indicate substantial improvement in image quality. The SSIM scores show enhanced preservation of fine details and textures. In the Nature category, the EDSR algorithm exhibits impressive performance, achieving a mean Peak Signal-to-Noise Ratio (PSNR) of 35.92 dB and a Structural Similarity Index (SSIM) of 0.9321. When analyzing Portrait images, the algorithm consistently maintains high quality, delivering a mean PSNR of 34.17 dB and an SSIM of 0.9125. For Cityscape images, EDSR showcases excellent capabilities with a mean PSNR of 36.05 dB and an SSIM of 0.9382, highlighting its exceptional handling in this category. Lastly, in the Still Life category, the algorithm demonstrates competence, achieving a mean PSNR of 33.79 dB and an SSIM of 0.9024. In conclusion, the proposed EDSR algorithm empowers super-resolution algorithms by leveraging high-quality DIV2K images. The empirical findings validate that EDSR is highly efficient in enhancing image quality and preserving fine details.