Real World Super Resolution

Real-world super-resolution (RWSR) aims to enhance the resolution of low-resolution images captured under uncontrolled, real-world conditions, unlike traditional methods that assume known degradation models. Current research focuses on developing robust models that generalize well to diverse and unknown degradations, often employing techniques like adversarial training, self-supervised learning from multiple camera zooms, and novel loss functions that incorporate perceptual quality metrics. These advancements are crucial for improving the quality of images in various applications, such as document scanning, medical imaging, and video enhancement, where high-resolution images are often unavailable or impractical to obtain directly.