# Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes

## TL;DR

This paper introduces a novel deep learning approach for single image deraining that estimates transmission and atmospheric light using an atmospheric scattering model, leading to improved rain removal performance.

## Contribution

It proposes a triangle-shaped network for estimating atmospheric light and integrates it with transmission estimation based on an atmospheric scattering model for better deraining results.

## Key findings

- Outperforms state-of-the-art deraining methods in subjective and objective evaluations.
- Utilizes a robust method for estimating atmospheric light in rainy scenes.
- Employs ShuffleNet Units for efficient transmission map learning.

## Abstract

Rain removal in images/videos is still an important task in computer vision field and attracting attentions of more and more people. Traditional methods always utilize some incomplete priors or filters (e.g. guided filter) to remove rain effect. Deep learning gives more probabilities to better solve this task. However, they remove rain either by evaluating background from rainy image directly or learning a rain residual first then subtracting the residual to obtain a clear background. No other models are used in deep learning based de-raining methods to remove rain and obtain other information about rainy scenes. In this paper, we utilize an extensively-used image degradation model which is derived from atmospheric scattering principles to model the formation of rainy images and try to learn the transmission, atmospheric light in rainy scenes and remove rain further. To reach this goal, we propose a robust evaluation method of global atmospheric light in a rainy scene. Instead of using the estimated atmospheric light directly to learn a network to calculate transmission, we utilize it as ground truth and design a simple but novel triangle-shaped network structure to learn atmospheric light for every rainy image, then fine-tune the network to obtain a better estimation of atmospheric light during the training of transmission network. Furthermore, more efficient ShuffleNet Units are utilized in transmission network to learn transmission map and the de-raining image is then obtained by the image degradation model. By subjective and objective comparisons, our method outperforms the selected state-of-the-art works.