Improved Image Deblurring based on Salient-region Segmentation

TL;DR

This paper introduces a saliency-based deblurring approach that segments salient regions and employs PDE models for spatially-variant image deblurring, demonstrating improved effectiveness in handling complex blur.

Contribution

The paper presents a novel saliency-based segmentation combined with PDE-based deblurring techniques for spatially-variant blur removal.

Findings

Effective handling of spatially-variant blur.

Improved deblurring quality demonstrated in experiments.

Integration of saliency detection with PDE models enhances results.

Abstract

Image deblurring techniques play important roles in many image processing applications. As the blur varies spatially across the image plane, it calls for robust and effective methods to deal with the spatially-variant blur problem. In this paper, a Saliency-based Deblurring (SD) approach is proposed based on the saliency detection for salient-region segmentation and a corresponding compensate method for image deblurring. We also propose a PDE-based deblurring method which introduces an anisotropic Partial Differential Equation (PDE) model for latent image prediction and employs an adaptive optimization model in the kernel estimation and deconvolution steps. Experimental results demonstrate the effectiveness of the proposed algorithm.