Laplacian encoder-decoder network for raindrop removal

Abstract

• Rain droplets removed by generating the Laplacian pyramid levels of rain-free images. • The Laplacian pyramid decomposition improves with respect to a simple encoder-decoder. • The training benefits from comparing each generated level with the ground truth. • Reconstructing levels before comparison, balances their impact and provides context. • Better high frequencies will improve structural similarity and signal-to-noise ratio. Digital raindrop removal is a branch of image restoration that aims at identifying adherent droplets on a glass surface and replacing them with plausible content. When successfully performed, raindrop removal was proven in the past to positively affect both the perceived appearance of the scene, and the performance of computer vision tasks such as semantic segmentation and object detection. In this paper, we design and implement a new encoder-decoder neural network for supervised raindrop removal. Our network, given a rainy input image, produces as output the Laplacian pyramid of a rain-free version of the input, making it possible to handle the variety of appearances of rain droplets by processing different frequency bands independently. To this end, we define and experimentally prove the effectiveness of a custom loss function that combines the errors of the different Laplacian frequency bands. We test our model for raindrop removal on a standard dataset, using multiple objective metrics to provide a detailed analysis of its performance. We confirm the superiority of our proposal in a comparison with other methods from the state of the art.