Robust Regression For Image Binarization Under Heavy Noises and Nonuniform Background
This addresses the problem of identifying foreground regions in noisy microscopic or degraded document images for researchers and practitioners in image processing, though it is incremental as it builds on existing regression and thresholding techniques.
The paper tackles image binarization under heavy noise and nonuniform background by proposing a robust regression method to estimate and subtract the background, followed by global thresholding, achieving improved results validated on 26 test images compared to nine existing methods.
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.