Remote sensing images are often polluted by stripe noise, which leads to negative impact on visual performance. Thus, it is necessary to remove stripe noise for the subsequent applications, e.g., classification and target recognition. This paper commits to remove the stripe noise to enhance the visual quality of images, while preserving image details of stripe-free regions. Instead of solving the underlying image by variety of algorithms, we first estimate the stripe noise from the degraded images, then compute the final destriping image by the difference of the known stripe image and the estimated stripe noise. In this paper, we propose a non-convex 0 sparse model for remote sensing image destriping by taking full consideration of the intrinsically directional and structural priors of stripe noise, and the locally continuous property of the underlying image as well. Moreover, the proposed non-convex model is solved by a proximal alternating direction method of multipliers (PADMM) based algorithm. In addition, we also give the corresponding theoretical analysis of the proposed algorithm. Extensive experimental results on simulated and real data demonstrate that the proposed method outperforms recent competitive destriping methods, both visually and quantitatively.Keywords: non-convex 0 sparse model; PADMM based algorithm; mathematical program with equilibrium constraints (MPEC); stripe noise removal
Restoring Poissonian noise images have drawn a lot of attention in recent years. There are many regularization methods to solve this problem and one of the most famous methods is the total variation model. In this paper, by adding a quadratic regularization on TGV regularization part, a new image restoration model is proposed based on second-order total generalized variation regularization. Then the split Bregman iteration algorithm was used to solve this new model. The experimental results show that the proposed model and algorithm can deal with Poisson image restoration problem well. What’s more, the restoration model performance is significantly improved both in visual effect and objective evaluation indexes.
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