Wavelet-based Auto-Encoder for simultaneous haze and rain removal from images

Abstract

Noise introduced due to weather can reduce the efficiency of computer vision applications as the visibility of the objects in images is greatly affected. Haze and rain are the most common weather conditions seen in nature. However, most of the algorithms found in the literature apply rain and haze removal approaches separately. To this end, in this paper, we propose a novel Wavelet-based deep Auto-encoder, called WAE, for simultaneously removing the haze and rain effects from images. The proposed network uses wavelet transformation and inverse wavelet transformation as an alternative to down-sampling and up-sampling operations, respectively, in order to add sparsity to the network. By training the model on both spatial and frequency domains, it learns non-stationary features that are found to be useful to remove haze and rain effects from images. The proposed model is tested on several rain and haze-affected image datasets, and it performs well in terms of standard evaluation metrics like structural similarity index measure and peak signal-to-noise ratio. The code can be found at : https://github.com/asfakali/WAE.git.