Variational Image Segmentation Model Coupled with Image Restoration Achievements

TL;DR

This paper introduces a novel multiphase image segmentation model that integrates image restoration techniques, effectively handling noisy, blurry, and incomplete images, and demonstrates superior performance over existing methods.

Contribution

The paper presents a new coupled segmentation-restoration model extending Mumford-Shah, with an efficient alternating minimization algorithm and proven convergence, improving segmentation robustness.

Findings

Outperforms state-of-the-art models on synthetic and real images.

Effectively handles noisy, blurry, and incomplete images.

Converges reliably under mild conditions.

Abstract

Image segmentation and image restoration are two important topics in image processing with great achievements. In this paper, we propose a new multiphase segmentation model by combining image restoration and image segmentation models. Utilizing image restoration aspects, the proposed segmentation model can effectively and robustly tackle high noisy images, blurry images, images with missing pixels, and vector-valued images. In particular, one of the most important segmentation models, the piecewise constant Mumford-Shah model, can be extended easily in this way to segment gray and vector-valued images corrupted for example by noise, blur or missing pixels after coupling a new data fidelity term which comes from image restoration topics. It can be solved efficiently using the alternating minimization algorithm, and we prove the convergence of this algorithm with three variables under mild condition. Experiments on many synthetic and real-world images demonstrate that our method gives better segmentation results in comparison to others state-of-the-art segmentation models especially for blurry images and images with missing pixels values.