Beyond Synthetic Data: A Blind Deraining Quality Assessment Metric Towards Authentic Rain Image

Deraining quality assessment (DQA) plays an important role in evaluating and guiding the design of the image deraining algorithm. Due to the absence of rain-free image in the real rainy weather, the existing deraining algorithms are typically tested on several synthetic data by simulating very limited types of rain streaks, which are far from sufficient to measure the practicability of a deraining algorithm. In this paper, we first build a subjective DQA database that collects diverse authentic rain images and their derained versions. Then, a blind quality metric is developed to predict the deraining quality. Since the deraining artifacts are anisotropic and variable, we propose to describe the image via a bi-directional gated fusion network (B-GFN), which adaptively integrates the multi-scale cues of deraining artifact. Experiments confirm the effectiveness of the proposed method and its superiority with respect to many state-of-the-art blind image quality metrics.