The field of image deblurring has seen tremendous progress with the rise of
deep learning models. These models, albeit efficient, are computationally
expensive and energy consuming. Dictionary based learning approaches have shown
promising results in image denoising and Single Image Super-Resolution. We
propose an extension of the Rapid and Accurate Image Super-Resolution (RAISR)
algorithm introduced by Isidoro, Romano and Milanfar for the task of
out-of-focus blur removal. We define a sharpness quality measure which aligns
well with the perceptual quality of an image. A metric based blending strategy
based on asset allocation management is also proposed. Our method demonstrates
an average increase of approximately 13% (PSNR) and 10% (SSIM) compared to
popular deblurring methods. Furthermore, our blending scheme curtails ringing
artefacts post restoration.

基于 Isidoro，Romano 和 Milanfar 引入的 RAISR 算法，我们提出了一种扩展的快速准确图像超分辨率算法，用于去除失焦模糊，并定义了一个与图像感知质量相一致的锐度质量度量。此外，我们提出了一种基于资产分配管理的度量融合策略，与流行的去模糊方法相比，我们的方法平均 PSNR 增加约 13％，SSIM 增加约 10％，同时我们的融合方案减少了恢复后的环状伪影。