An Efficient Low Complex-Functional Link Artificial Neural Network-Based Framework for Uneven Light Image Thresholding

The most popular technique for converting two-class images into binary images is thresholding. However, thresholding methods tend to perform poorly when dealing with images affected by uneven lighting. To address this issue, local thresholding techniques are commonly used. While pixel-based local thresholding methods can achieve high accuracy, they are computationally complex. Window-based local thresholding presents challenges in selecting the initial window and determining the criterion function for dividing the image into smaller versions. In this study, a novel technique is proposed to improve the effectiveness of binarizing images with uneven lighting. The proposed method is based on a low-complexity functional neural network model (LC-FLANN) to estimate an image’s illumination surface. The effectiveness of the proposed technique has been evaluated using five widely used uneven lighting image binarization techniques and various uneven light image variations. The results show that the proposed approach outperforms other alternatives in both qualitative and quantitative metrics. It achieved an average F-Measure score of 0.97, a Jaccard Index (JI) score of 0.95, and a Percentage of Misclassification Error (PME) 1.42%, demonstrating superior overall performance.