A Dictionary Based Approach for Removing Out-of-Focus Blur

TL;DR

This paper introduces a dictionary-based extension of the RAISR algorithm for removing out-of-focus blur, achieving improved image quality with reduced artifacts compared to existing methods.

Contribution

It extends the RAISR algorithm for out-of-focus blur removal, incorporating a perceptual sharpness measure and a blending strategy to enhance results and reduce artifacts.

Findings

Average 13% PSNR improvement over existing methods.

Average 10% SSIM improvement.

Reduces ringing artifacts post-restoration.

Abstract

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.