Single Image Super-Resolution with Transformer-Based Architectures: A Deep Dive
Published:
Single Image Super-Resolution (SISR) is one of the fundamental problems in low-level computer vision: given a low-resolution (LR) input, reconstruct a high-resolution (HR) output that is visually faithful and perceptually sharp. For years, convolutional neural networks (CNNs) dominated this space. But since the rise of the Vision Transformer (ViT), the field has shifted dramatically — and Transformer-based SR models now hold state-of-the-art results across virtually every benchmark.