Zero-Shot Restoration of Back-lit Images Using Deep Internal Learning

How to restore back-lit images still remains a challenging task. State-of-the-art methods in this field are based on supervised learning and thus they are usually restricted to specific training data. In this paper, we propose a "zero-shot" scheme for back-lit image restoration, which exploits the power of deep learning, but does not rely on any prior image examples or prior training. Specifically, we train a small image-specific CNN, namely ExCNet (short for Exposure Correction Network) at test time, to estimate the "S-curve" that best fits the test back-lit image. Once the S-curve is estimated, the test image can be then restored straightforwardly. ExCNet can adapt itself to different settings per image. This makes our approach widely applicable to different shooting scenes and kinds of back-lighting conditions. Statistical studies performed on 1512 real back-lit images demonstrate that our approach can outperform the competitors by a large margin. To the best of our knowledge, our scheme is the first unsupervised CNN-based back-lit image restoration method. To make the results reproducible, the source code is available at https://cslinzhang.github.io/ExCNet/.