A Dictionary Based Approach for Removing Out-of-Focus Blur
This work addresses the problem of computational efficiency and energy consumption in image deblurring for applications requiring fast processing, though it is incremental as it builds on existing dictionary-based methods.
The paper tackled out-of-focus blur removal in images by extending the RAISR algorithm with a sharpness quality measure and a metric-based blending strategy, resulting in an average increase of approximately 13% in PSNR and 10% in SSIM compared to popular deblurring methods.
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.