Single-image de-raining using joint filter and multi-scale deep alternate-connection dense network

Abstract This paper aims at developing an effective approach to remove rain layer from an individual image. The proposed is a two-stage method named JF-MADN consisting of a Joint Filter (JF) extracting high frequency details from rainy image and a Multi-scale deep Alternate-connection Dense Network (MADN) separating rain streaks in a coarse-to-fine manner. In the first stage, rainy image is enhanced first through a high frequency emphasis filter. Next, a gradient operator is deduced based on trigonometric function and Laplacian operator to generate rain gradient prior. By combining it with rain dark channel prior, a rain-prior weighted statistic order filter could be constructed to separate high and low frequency components of rainy image. Then, the result image through a guided filter followed by would be subtracted by the original low frequency component to obtain residuals for final operation with high frequency component to generate high quality initial rain layer. In the second stage, by adopting multi-scale mechanism, this study develops two new alternate-connected fully convolutional DenseNets in different scale, WNet-129 and NNet-96, to implement de-raining. By combining element-wise addition in our alternate-connection structure with original concatenation in fully convolutional DenseNet, features could be constantly connected in our network. Experimental results demonstrate that the proposed framework outperforms existing single-image de-raining algorithms on testing rainy images. Code available at: http://www.imagetech-polynomials.com/derain.html.