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首页> 外文期刊>Journal of visual communication & image representation >High-quality non-blind image deconvolution with adaptive regularization
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High-quality non-blind image deconvolution with adaptive regularization

机译：具有自适应正则化的高质量非盲图像反卷积

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摘要

Non-blind image deconvolution is a process that obtains a sharp latent image from a blurred image when a point spread function (PSF) is known. However, ringing and noise amplification are inevitable artifacts in image deconvolution since perfect PSF estimation is impossible. The conventional regularization to reduce these artifacts cannot preserve image details in the deconvolved image when PSF estimation error is large, so strong regularization is needed. We propose a non-blind image deconvolution method which preserves image details, while suppressing ringing and noise artifacts by controlling regularization strength according to local characteristics of the image. In addition, the proposed method is performed fast with fast Fourier transforms so that it can be a practical solution to image deblurring problems. From experimental results, we have verified that the proposed method restored the sharp latent image with significantly reduced artifacts and it was performed fast compared to other non-blind image deconvolution methods.
机译：非盲图像反卷积是一种在已知点扩展函数（PSF）的情况下从模糊图像中获得清晰的潜像的过程。但是，由于不可能进行完美的PSF估计，因此振铃和噪声放大是图像反卷积中不可避免的伪影。当PSF估计误差很大时，减少这些伪像的常规正则化方法无法在反卷积图像中保留图像细节，因此需要强大的正则化方法。我们提出了一种非盲图像反卷积方法，该方法可保留图像细节，同时通过根据图像的局部特征控制正则强度来抑制振铃和噪声伪影。此外，该方法可通过快速傅立叶变换快速执行，因此可以作为解决图像模糊问题的实用方法。从实验结果来看，我们已经验证了所提出的方法可以还原锐利的潜像并减少了伪像，并且与其他非盲图像反卷积方法相比，该方法可以快速执行。

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