Training a Task-Specific Image Reconstruction Loss
This work addresses the need for specialized loss functions in image restoration to improve perceptual quality, though it is incremental as it builds on existing discriminator-based methods.
The authors tackled the problem of designing effective loss functions for image restoration tasks by proposing to train application-specific loss functions that penalize task-specific artifacts, achieving state-of-the-art performance in super resolution, denoising, and JPEG artifact removal with just a single natural image and its distortions.
The choice of a loss function is an important factor when training neural networks for image restoration problems, such as single image super resolution. The loss function should encourage natural and perceptually pleasing results. A popular choice for a loss is a pre-trained network, such as VGG, which is used as a feature extractor for computing the difference between restored and reference images. However, such an approach has multiple drawbacks: it is computationally expensive, requires regularization and hyper-parameter tuning, and involves a large network trained on an unrelated task. Furthermore, it has been observed that there is no single loss function that works best across all applications and across different datasets. In this work, we instead propose to train a set of loss functions that are application specific in nature. Our loss function comprises a series of discriminators that are trained to detect and penalize the presence of application-specific artifacts. We show that a single natural image and corresponding distortions are sufficient to train our feature extractor that outperforms state-of-the-art loss functions in applications like single image super resolution, denoising, and JPEG artifact removal. Finally, we conclude that an effective loss function does not have to be a good predictor of perceived image quality, but instead needs to be specialized in identifying the distortions for a given restoration method.