A Haze Intensity Sensing and Texture Enhanced Transformer for Image Dehazing

The degradation of images captured in hazy weather can severely affect practical applications. However, most existing learning-based methods ignore the varied haze distribution in an image, resulting in incomplete dehazing of some areas. Also, the presence of haze can blur the textures and details, which will heavily impact the subsequent image processing. In this paper, we propose a transformer-based framework for dehazing tasks called HITFormer. Firstly, we introduce a texture recovery and enhance module as a preprocess to strengthen details. Then, we propose an adaptive haze intensity prediction subnet to predict the haze intensity of different areas. Lastly, we use a semantic-based luminance and chrominance adjustment module to fuse the feature maps in YUV color space and form a transformation coefficient to get a recovery image. The extensive experiments demonstrate that our HITFormer achieves state-of-the-art performance on several image dehazing datasets.