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Objective: To use computational techniques for removing rain from images. This is motivated by the fact that for many computer vision systems, correctly capturing the scene is a key need, and if such systems receive images degraded by rain as input, their performance may be compromised.  Methodology: We built a dataset comprised by 11000 synthetic rain images. We resized and normalized all the images, then we employed 9000 of them for training the autoencoder architecture. The autoencoder outputs a de-rained version of the image which is then lighting corrected in order to produce the final, de-rained image. Results: We determined the best autoencoder architecture was a 6-layer autoencoder. We evaluated it on the remaining 2000 images, resulting in a Mean Squared Error of 0.61 and Structural Similarity Index of 0.8493, which means a fair amount of information from the rain-degraded images was recovered. Conclusions: The results we obtained were superior to proposals based in the spatial / frequency domain reported in the literature. However, we determined that it is possible to improve on the current results if we consider the frequency domain as part of the architecture. Thus, options for future work include combining machine learning-based approaches with frequency domain-based image processing.

Remoción de lluvia en imágenes por medio de una arquitectura de autoencoder