基于正则化约束的遥感图像多尺度去模糊

Remote sensing image multi-scale deblurring based on regularization constraint

目的遥感成像过程中的图像降质严重影响了高分辨率成像与高精度探测,为了改善遥感图像质量,提出了基于正则化约束的遥感图像多尺度去模糊方法。方法首先利用双边滤波器和冲击滤波对遥感图像进行预处理,然后结合遥感图像模糊核的稀疏特性,使用正则化方法迭代求解模糊核最优解,最后利用基于梯度稀疏的非盲反卷积方法得到去模糊结果。此外,针对图像模糊程度较严重的情况,分析了尺度信息对去模糊结果的影响,提出了多尺度迭代优化方法。结果采用本文方法对大量遥感图像进行去模糊,实验结果表明该方法能有效地去除遥感成像产生的模糊,在保持图像边缘和细节的同时,可有效抑制振铃效应。相比其他方法,本文方法恢复图像的边缘强度平均提高28.7%,对比度平均提高17.6%。结论提出一种正则化约束的遥感图像多尺度去模糊方法,主观视觉感受和客观评价指标都表明该方法可以有效提升遥感图像质量。

Objective Image degradation during remote photography severely affects high-resolution imaging and high-accuracy detection. To improve the quality of remote sensing images, a multi-scale image deblurring method for remote sensing viaregularization eonstraintsis proposed in this paper. Method At the beginning of deblurring, bilateral and shock filtersare used to handle blurred images. Subsequently, a variational Bayesian iterative model is applied to determine the optimal solution by considering prior knowledge of the sparsity feature of the blur kernel. Finally, the deblurring result can be obtained by non-blind deconvolution based on gradient sparsity. In addition, the effect of scale information on the deblurring resuh is studied for the case of a serious blur, and a multi-scale iterative method is proposed. Result Our algorithm is implemented for deblurring numerous remote sensing images. Experimental results show that the proposed method can effec- tively remove fuzzy sections, maintain edges, and recover details of blurred images. Other methods are compared with the proposed algorithm. Indices such as entropy, contrast ratio （CR）, edge strength level （ESL） , and HSV （hue, saturation, value） model are used in the objective evaluation. The ESL average of the images increases by 28.7% , where as the CR average increases by 17.6% after using our method. Conclusion Subjective visual experience and objective evaluation indices show that the proposed method can effectively improve the quality of remote sensing images.