Attention-based Adaptive Selection of Operations for Image Restorationin the Presence of Unknown Combined Distortions
Abstract Many studies have been conducted so far on image
restoration, the problem of restoring a clean image from
its distorted version. There are many different types of distortion which affect image quality. Previous studies have
focused on single types of distortion, proposing methods for
removing them. However, image quality degrades due to
multiple factors in the real world. Thus, depending on applications, e.g., vision for autonomous cars or surveillance
cameras, we need to be able to deal with multiple combined
distortions with unknown mixture ratios. For this purpose,
we propose a simple yet effective layer architecture of neural networks. It performs multiple operations in parallel,
which are weighted by an attention mechanism to enable
selection of proper operations depending on the input. The
layer can be stacked to form a deep network, which is differentiable and thus can be trained in an end-to-end fashion by gradient descent. The experimental results show that
the proposed method works better than previous methods by
a good margin on tasks of restoring images with multiple
combined distortions.