Multi-Scale Dehaze Network Based on Frequency Domain Assistance and Detailed Brightness Information Guidance

Abstract—Optical image dehazing is a challenging task. Although both physical model-based and deep learning-based dehazing methods have achieved a certain degree of restoration results, they are all reflected in a single spatial domain processing, and deep learning methods have the problem of large models. We start from the flow of information, aiming to combine pixel and frequency domain information, redesign the up- and downsampling mechanism based on discrete wavelet transform, and separate the frequency spectrum of features in the neck network for auxiliary processing, making full use of frequency domain information to assist the dehazing process. Considering the basic nature of the dehazing task in image processing, we introduce feature fusion guided by brightness and texture information to restore higher quality clear and haze-free images. We conducted experiments on SOTS-Indoor, NH-haze, and Dense-haze datasets. The results show that on the Indoor dataset, as shown in Fig.1, Our model parameters and computational costs are reduced by at least 60%, achieving a 41.11dB effect with only 16.48GFlops of computational cost and 1.89M parameters.