Single image reflection removal via self-attention and local discrimination

In practical scenarios, reflections may impair the visual quality of images, bring negative impacts to both human perception and subsequent computer vision tasks. Removing reflections from an image poses considerable challenges due to the diverse nature of reflection content, often blended with background targets. To address the issue, this paper proposes a single-image reflection removal method, which is based on self-attention and local discrimination. By exploiting the mutual relationship between feature maps with a self-attention mechanism, the proposed method can adaptively re-weights each feature according to the input content, benefiting the separation of reflections. For further improvement, a scaled variance map is generated from local statistics and then used in a local discrimination loss to ensure the consistency within local regions. Experimental comparisons have demonstrated the effectiveness and superiority of the proposed method for single image reflection removal. The related code is released at https://github.com/HighColdMan/SIRR_SALD.