A Study on Resolution Enhancement of CNN through Edge Loss Compensation in High-Magnification Upscaling

Recent single image super-resolution research plays a crucial role in fields such as medical imaging, satellite imaging, and surveillance. Although the VDSR(Very Deep Super Resolution) model achieves excellent restoration performance through deep convolutional neural networks(CNN) and residual learning , its reliance on pixel-level MSE loss limits its ability to recover high-frequency details—resulting in blurry images, especially at 3× and 4× upscaling. To overcome this limitation, we propose a method that integrates an edge loss term into the VDSR framework. By using a Sobel filter, we extract horizontal and vertical edge maps and compute the L1 loss between the edge maps of the super-resolved image and the ground truth. This additional loss encourages the model to restore both pixel accuracy and fine structural details. Experimental results on the Set5 dataset under multiple upscaling scales indicate that performance gains in PSNR and SSIM become more pronounced as the scaling factor increases.