Robust Regression For Image Binarization Under Heavy Noises and Nonuniform Background

TL;DR

This paper introduces a robust regression-based method for image binarization that effectively handles heavy noise and nonuniform backgrounds, improving segmentation accuracy in challenging microscopic and degraded document images.

Contribution

The paper proposes a novel robust regression approach for background estimation and a model selection criterion for thresholding, enhancing binarization under severe noise and background variation.

Findings

Outperforms nine existing binarization methods on test images.

Improves segmentation results when combined with morphological methods.

Validated on 26 images with ground truth comparisons.

Abstract

This paper presents a robust regression approach for image binarization under significant background variations and observation noises. The work is motivated by the need of identifying foreground regions in noisy microscopic image or degraded document images, where significant background variation and severe noise make an image binarization challenging. The proposed method first estimates the background of an input image, subtracts the estimated background from the input image, and apply a global thresholding to the subtracted outcome for achieving a binary image of foregrounds. A robust regression approach was proposed to estimate the background intensity surface with minimal effects of foreground intensities and noises, and a global threshold selector was proposed on the basis of a model selection criterion in a sparse regression. The proposed approach was validated using 26 test images and the corresponding ground truths, and the outcomes of the proposed work were compared with those from nine existing image binarization methods. The approach was also combined with three state-of-the-art morphological segmentation methods to show how the proposed approach can improve their image segmentation outcomes.