The key to the restoration of rotational motion blurred image is how to restore the image under a low cost and to correct the irreversibility of the degradation function matrix. Based on the special qualities of degra...The key to the restoration of rotational motion blurred image is how to restore the image under a low cost and to correct the irreversibility of the degradation function matrix. Based on the special qualities of degradation function matrix and precise deduction in space-domain, we present a new approach using gradient-loading for restoration of rotational blurred image. By easily adding a gradient operator, the irreversibility of the original matrix is corrected and can be applied for inverse filtering then. Gradient- loading is the optimized approach which combines the advantages of both the approaches using constrained least square filtering and traditional diagonal-loading. Compared with the approach using least square filtering, its peak signal-to-noise ratio （PSNR） is improved from 3.18 to 6.46 dB, while the computing time is reduced to 1/2 - 1/3. Experimental results demonstrate the effectiveness, noise-resistibility, robustness, and low complexity of this approach, which make it more suitable for real-time environment.展开更多
基金the National Key Lab-oratory Scientific Foundation of Optical Signature of Targets and Environments and Cultivation Fund of the Key Scientific and Technical Project,Ministry of Educa-tion of China (No.706022).
文摘The key to the restoration of rotational motion blurred image is how to restore the image under a low cost and to correct the irreversibility of the degradation function matrix. Based on the special qualities of degradation function matrix and precise deduction in space-domain, we present a new approach using gradient-loading for restoration of rotational blurred image. By easily adding a gradient operator, the irreversibility of the original matrix is corrected and can be applied for inverse filtering then. Gradient- loading is the optimized approach which combines the advantages of both the approaches using constrained least square filtering and traditional diagonal-loading. Compared with the approach using least square filtering, its peak signal-to-noise ratio （PSNR） is improved from 3.18 to 6.46 dB, while the computing time is reduced to 1/2 - 1/3. Experimental results demonstrate the effectiveness, noise-resistibility, robustness, and low complexity of this approach, which make it more suitable for real-time environment.
