Single Image Haze Removal Using Deep Cellular Automata Learning

Deep learning is one of the most popular approaches to machine learning, which has been widely used for classification. In this paper, we propose a novel learning method based on a combination of an idea of the deep learning approach and the cellular automata model, called DeepCA for single image haze removal. DeepCA's learning is divided into two main parts. The first part is a cellular automata-based deep feature extraction: multi-layer cellular automata with the rules are used to extract the data feature matrices of the image, in which the matrices can be divided into several layers. Then, the score matrices were generated as the model in which was trained by the cellular automata rules. The second part is a decision stage: we used the score matrices to the mapping between the proper data. For demonstration, we take the single image haze removal task as an example to confirm the capability of the proposed method. In this regard, the dichromatic model is chosen as the major model to remove the haze of the image. The multi-layer cellular automata with the rules work as a mechanical extractor of the light source feature of the hazy image. The decision stage of DeepCA performs as the recognizer for properly predicting the global light source for dehazing. This aims to improve the light source and the transmission map that they are important compositions for haze-free image restoration. For performance evaluation, we perform quantitative and qualitative measures. For the qualitative performance of the haze removal, DeepCA did not even cause the halo artifact effect that occurred in other haze removal algorithms. The empirical results in quantitative measures show that DeepCA improved intensity, color saturation quality, and halo artifact when compared with the state-of-the-art methods.