A dehazing method for flight view images based on transformer and physical priori

Aiming at the problems of local dehazing distortion and incomplete global dehazing of existing algorithms in real airborne cockpit environments, a two-stage dehazing method PhysiFormer combining physical a priori with a Transformer oriented flight perspective was proposed. The first stage used synthetic pairwise data to pre-train the dehazing model. First, a pyramid pooling module (PPM) was introduced in the Transformer for multiscale feature extraction to solve the problem of poor recovery of local details, then a global context fusion mechanism was used to enable the model to better perceive global information. Finally, considering that combining the physical a priori needs to rely on the estimation of the atmosphere light, an encoding-decoding structure based on the residual blocks was used to estimate the atmosphere light, which was then used for dehazing through the atmospheric scattering model for dehazing. The second stage used real images combined with physical priori to optimize the model to better fit the real airborne environment. The experimental results show that the proposed method has better naturalness image quality evaluator (NIQE) and blind/referenceless image spatial quality evaluator (BRISQUE) indexes and exhibits the best dehazing visual effect in the tests of dense haze, non-uniform haze and real haze images, which effectively improves the problems of color distortion and haze residue.