Exploiting image structural similarity for single image rain removal

Without any prior knowledge or user interaction, single image rain removal has been a challenging task. Typically, one needs to disregard image components associated with the rain patterns, so that rain removal can be achieved via image reconstruction. By observing the limitations of standard batch-mode learning-based methods, we propose to exploit the structural similarity of the image bases for solving this task. By formulating the basis selection as an optimization problem, we are able to disregard those associated with rain patterns while the detailed image information can be preserved. Experiments on both synthetic and real-world images will verify the effectiveness of our proposed method.