Image Enhancement & Super-Resolution Challenge
25-02-2026 09:30AM
In many real-world scenarios — surveillance systems, medical imaging, satellite monitoring, and mobile photography — captured images suffer from low resolution, noise, blur, and unknown degradation processes. Enhancing low-quality images into high-resolution, visually faithful outputs is a core challenge in computer vision and generative AI.
Participants must build a model that takes Low-Resolution (LR) images as input and outputs High-Resolution (HR) enhanced images, upscaling by a factor of ×2, handling unknown degradation operators.
The dataset consists of paired images: High-Resolution Images (HR) as ground truth targets, and Low-Resolution Images (LR) generated using unknown degradation operators including Gaussian blur, noise injection, compression artifacts, and mixed distortions.
Participants must learn the degradation mapping implicitly.
Upscale LR images by a factor of 2 and reconstruct high-quality HR outputs. No knowledge of the degradation process will be provided.
Pretrained models are allowed but must be disclosed.