Attention mechanism enhancement algorithm based on cycle consistent generative adversarial networks for single image dehazing

This paper proposes AMEA-GAN, an attention mechanism enhancement algorithm. It is cycle consistency-based generative adversarial networks for single image dehazing, which follows the mechanism of the human retina and to a great extent guarantees the color authenticity of enhanced images. To address the color distortion and fog artifacts in real-world images caused by most image dehazing methods, we refer to the human visual neurons and use the attention mechanism of similar Horizontal cell and Amazon cell in the retina to improve the structure of the generator adversarial networks. By introducing our proposed attention mechanism, the effect of haze removal becomes more natural without leaving any artifacts, especially in the dense fog area. We also use an improved symmetrical structure of FUNIE-GAN to improve the visual color perception or the color authenticity of the enhanced image and to produce a better visual effect. Experimental results show that our proposed model generates satisfactory results, that is, the output image of AMEA-GAN bears a strong sense of reality. Compared with state-of-the-art methods, AMEA-GAN not only dehazes images taken in daytime scenes but also can enhance images taken in nighttime scenes and even optical remote sensing imagery.