  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.
