An end-to-end multi-resolution feature fusion defogging network

Traditional convolutional neural networks work well on single-image defogging synthetic datasets, but for real-world images with different concentrations, it leads to incomplete defogging or distortion and loss of image detail information. In this paper, the authors propose an end-to-end single-image defogging network, which adopts an encoder–decoder structure, obtains a large perceptual field of view of high pixels through a large number of pooling operations in the U-Net network, and uses operations such as hopping connections to retain most of the image feature information. To achieve superior quality haze removal, the proposed method utilizes a bilateral grid to capture high-frequency information pertaining to the image edges in low-resolution pixels. Additionally, relevant haze-related features are extracted, and a local affine model is fitted within the bilateral space. Finally, the high and low pixel data are integrated with the extracted features to generate clear and vivid images. The authors compare the algorithm qualitatively and quantitatively with several state-of-the-art algorithms and show that the algorithm achieves better defogging results in both the SOTS dataset and real-world images, retains high-frequency image details, achieves higher peak signal-to-noise ratio, and performs better defogging in haze images with different concentrations.