Fast Restoration Algorithm for Space-variant Defocus Blurred Infrared Images
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摘要: 为了提升空间变化离焦模糊红外图像的图像质量，提出了一种基于图像质量评价的快速复原算法。本文提出的方法首先对模糊图像采用不同点扩散函数对应的截断约束最小二乘法算法进行复原而获得多幅复原图像，并对复原图像进行去振铃；然后对复原图像中每个像素为中心的区域进行图像质量评价，将采用不同参数复原的图像以图像质量评价的结果进行组合以获得最终的复原图像。由于无需对模糊图像点扩散函数估计，且采用了空间域运算的截断约束最小二乘法算法进行图像复原，实验结果表明，本文提出的算法能够对空间变化离焦模糊红外图像进行快速复原，算法运行速度较基于点扩散函数估计的方法大幅提升。Abstract: A fast restoration algorithm based on image quality assessment is proposed to improve the quality of space-variant defocus blurred infrared images. First, the defocus image is restored by the truncated constrained least-squares algorithm with different point spread functions to obtain and perform deringing on multiple restored images. Then, the area centered on each pixel in the restored image is evaluated through an image quality assessment, and the images restored with different parameters are combined according to the image quality assessment to obtain the final restored image. Because there is no need to estimate the point spread function of the blurred image, the truncated constrained least-squares algorithm of spatial calculation is used for restoration. The experimental results show that the algorithm proposed in this paper can quickly restore the space-variant defocused blurred infrared image and is much faster than the method based on point spread function estimation.
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Key words:
- defocus /
- space-variant blur /
- image restoration /
- image quality assessment
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表 1 复原图像质量评价对比
Table 1. Comparison of image quality assessment of restoration algorithms
Entropy(image 1) Entropy(image 2) Std(image 1) Std(image 2) Input image 6.8712 6.9662 0.1951 0.1461 Zhang’s algorithm 6.8777 6.9854 0.1953 0.1649 Cheong’s algorithm 7.0675 7.3387 0.2150 0.2098 Proposed algorithm 6.9690 7.2084 0.2008 0.1666 
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表 2 复原算法运行时间对比
Table 2. Comparison of running time of restoration algorithms
S Blurred image 1 Blurred image 2 Zhang’s algorithm 272.955 297.082 Cheong’s algorithm 7.405 7.937 Proposed algorithm 0.711 0.796
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