A Sharpness Based Loss Function for Removing Out-of-Focus Blur

TL;DR

This paper introduces a sharpness-based loss function utilizing a no-reference sharpness metric to improve out-of-focus image removal, demonstrating enhanced perceptual quality and sharper restorations over existing methods.

Contribution

The paper proposes a novel sharpness-based loss function using a no-reference metric and introduces a new dataset for out-of-focus images, improving restoration quality.

Findings

7.5% increase in perceptual quality (LPIPS)

6.7% increase in sharpness metric Q

7.25% increase in PSNR

Abstract

The success of modern Deep Neural Network (DNN) approaches can be attributed to the use of complex optimization criteria beyond standard losses such as mean absolute error (MAE) or mean squared error (MSE). In this work, we propose a novel method of utilising a no-reference sharpness metric Q introduced by Zhu and Milanfar for removing out-of-focus blur from images. We also introduce a novel dataset of real-world out-of-focus images for assessing restoration models. Our fine-tuned method produces images with a 7.5 % increase in perceptual quality (LPIPS) as compared to a standard model trained only on MAE. Furthermore, we observe a 6.7 % increase in Q (reflecting sharper restorations) and 7.25 % increase in PSNR over most state-of-the-art (SOTA) methods.