Inharmonious Region Localization via Recurrent Self-Reasoning

TL;DR

This paper introduces a Recurrent Self-Reasoning (RSR) module integrated into a UNet architecture to effectively localize inharmonious regions in synthetic images, enhancing image quality by identifying inconsistent regions.

Contribution

The novel RSR module enables pixel clustering into inharmonious and background regions within a UNet framework, improving localization accuracy in synthetic image analysis.

Findings

Achieves competitive performance on image harmonization datasets.

Effectively localizes inharmonious regions with improved accuracy.

Demonstrates both quantitative and qualitative improvements.

Abstract

Synthetic images created by image editing operations are prevalent, but the color or illumination inconsistency between the manipulated region and background may make it unrealistic. Thus, it is important yet challenging to localize the inharmonious region to improve the quality of synthetic image. Inspired by the classic clustering algorithm, we aim to group pixels into two clusters: inharmonious cluster and background cluster by inserting a novel Recurrent Self-Reasoning (RSR) module into the bottleneck of UNet structure. The mask output from RSR module is provided for the decoder as attention guidance. Finally, we adaptively combine the masks from RSR and the decoder to form our final mask. Experimental results on the image harmonization dataset demonstrate that our method achieves competitive performance both quantitatively and qualitatively.