Image Super-Resolution with Deep Learning: Enhancing Visual Quality using SRCNN
Keywords:
Deep Learning, Image Super-Resolution, Quantitative Evaluation, SRCNN, Visual Quality Assessment.Abstract
This research digs into the space of Image Super-Resolution, particularly centring on the assessment and comparison of four noticeable calculations: SRCNN, FSRCNN, ESPCN, and VDSR. The study utilizes a different dataset enveloping different spaces, from common scenes to restorative imaging and satellite applications. Quantitative measurements, counting Top Signal-to-Noise Proportion (PSNR) and Structural Similarity Index (SSIM), and nearby visual quality evaluations were utilized to gauge the execution of each algorithm. SRCNN developed as the frontrunner, showing the most elevated PSNR of 28.7 and a commendable SSIM of 0.89. FSRCNN and ESPCN were closely taken after with PSNR values of 27.9 and 28.3, and SSIM scores of 0.88 and 0.87, separately. VDSR illustrated competitive execution with a PSNR of 27.5 and an SSIM of 0.86. These quantitative results were complemented by visual quality appraisals, where SRCNN got the most elevated rating of 9.2, followed by FSRCNN (8.8), ESPCN (8.7), and VDSR (8.5). This research contributes to the continuous exchange of computer vision, emphasizing the qualities and trade-offs of each calculation and giving important bits of knowledge into their pertinence over differing picture super-resolution scenarios.
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