Enhancing super resolution via frequency domain losses

Single-Image Super-Resolution (SISR) is a technique used to create high-resolution images from low-resolution ones. However, since the low-resolution images lack high-frequency components compared to their high-resolution counterparts, recreating the missing information becomes a crucial task. To address this, we propose directly penalizing the training loss in the frequency domain, in addition to the spatial domain. Our approach involves introducing an adversarial loss for training patches that are converted to the frequency domain using the Discrete Cosine Transform. We use a discriminator consisting of a convolutional neural network for this purpose. We also incorporate the Wavelet-domain High-Frequency Loss, which emphasizes the high-frequency spectrum. Our experiments have demonstrated that our approach can improve both quantitative and qualitative outcomes.