Robust regression for image binarization under heavy noise and nonuniform background. (September 2018)

• Record Type: Journal Article
• Authors: Vo, Garret D.; Park, Chiwoo
• Is Part Of: Pattern recognition. Volume 81(2018:Sep.)
• Page Start: 224
• Page End: 239
• Publication Date: 2018-09
• Subjects: Image binarization -- Background subtraction -- Robust regression -- Document image analysis -- Microscopy image analysis; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.04.005
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: This paper advances the background subtraction approach for image binarization. Our approach formulates a robust regression to estimate an image background. The proposed approach does not require any prior identification of edge pixels. The propose threshold selector binarizes noisy images better after background subtraction. The approach was validated with 26 benchmark images, comparing to nine existing methods. Abstract: This paper presents a robust regression approach for image binarization under significant background variations and observation noise. The work is motivated by the need of identifying foreground regions in noisy microscopic images or degraded document images, where significant background variations and observation noise make image binarization challenging. The proposed method first estimates the background of an input image, subtracts the estimated background from the input image, and performs a global thresholding operation to the subtracted outcome thus achieving the binary image of the foreground. A robust regression approach is proposed to estimate the background intensity surface with minimal effects of the foreground intensities and observation noise, and a global threshold selector is proposed on the basis of a model selection criterion in a sparse regression. The proposed approach is validated using 26 test images and the corresponding ground truths, and the outcomes are compared with those of nine existing image binarization methods. The approach is also combined with three morphological segmentation methods to show how the proposed approach can improve their image segmentation outcomes.