In order to improve the image quality, in this paper, we propose an improved PM model. In the proposed model, we introduce two novel diffusion coefficients and a residual error term and replace the integer differential operator with the fractional differential operator in the PM model. The diffusion coefficients can be used effectively for edge detection and noise removal. The residual error term can help to prevent image distortion. Fractional order differential operator has a good characteristic that it can enhance image texture information while removing image noise. Additionally, in the two new diffusion coefficients, a novel method is proposed for automatically setting parameter k, and it does not need to do any experiments to get the value of k. For the computing fractional order diffusion coefficient, we employ the discrete Fourier transform, and an iterative scheme is carried out in the frequency domain. In the proposed model, not only is the integer differential operator replaced with the fractional differential operator, but also the order of the fractional differentiation is determined adaptively with the local variance. Comparing with some existing models, the experimental results show that the proposed algorithm can not only better suppress noise, but also better preserve edge and texture information. Moreover, the running time is greatly reduced.

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The unmanned aerial vehicle (UAV) image taken in foggy or haze weather usually has lower contrast and fidelity, the quality of the image is seriously degraded. In this paper, we propose a dehazing model based on dark channel prior to dehaze the UAV image. We use a quad-tree hierarchical searching method to estimate the atmospheric light value, it can effectively avoid the influence of white objects in the image on the estimation of atmospheric light values. In the process of refining the medium transmission map, we propose a new regularization optimization scheme. Experimental results show that the proposed approach effectively recover the clear UAV image.