Mutual channel prior guided dual-domain interaction network for single image raindrop removal.

Removing raindrops adhering to lenses or glass is challenging since raindrops have more complex internal structures and optical effects than rain streaks. Existing raindrop removal methods typically employ single-channel masks for raindrop representation. However, these methods ignore the complementarity of priors between different channels, thereby losing the color and transparency information of raindrops. Moreover, simply applying the same processing across different pixels is insufficient to deal with spatially-varying degradation. To address these limitation, we propose a novel Mutual channel prior guided Dual-domain Interaction Network (MDINet) for single image raindrop removal. Specifically, to effectively guide the restoration of the raindrop region, we propose a mutual channel prior by modeling the multi-channel raindrop soft mask, in which the enriched color and transparency information of raindrops are represented and learned by our model. To tackle the spatially-varying degradation, we design the dual-domain interaction block to capture global low-frequency content and local high-frequency details. Simultaneously, a spatially adaptive modulation block is devised to handle complicated and diverse raindrops. Moreover, we propose a contrastive discrimination strategy to motivate the restored image to approach the clean image and stay away from the raindrop image in the representation space. Extensive experiments on benchmark datasets and real-world images indicate that the proposed method achieves qualitative and quantitative superiority against state-of-the-art methods. [Display omitted] • We propose a novel mutual channel prior for raindrop representation. • We design a dual-domain interaction network to tackle diverse raindrops. • Contrastive discrimination is proposed to mine the mutually exclusive property. [ABSTRACT FROM AUTHOR]