Work

Fast bilateral weighted least square for the detail enhancement of COVID-19 chest X-rays

Year: 2023

Type: article

Abstract: Background X-ray is an effective measure in the diagnosis of coronavirus disease 2019. However, it suffers from low visibility and poor details. A plausible solution is to decompose the captured images and enhance the details. The bilateral weighted least square model can be an effective tool for this task. However, it is highly computationally expensive. Method In this article, we propose an efficient algorithm for the bilateral weighted least square model. We approximate the bilateral weight with the bilateral grid and then incorporate it into the optimization model. This significantly reduces the number of variables in the linear system. Therefore, the model can be efficiently solved. We employ the proposed algorithm to decompose the input X-rays into base and detail layers. The detail layers are then boosted and added back to the input to derive the detail-enhanced results. Results The subjective results indicate that our method achieves higher contrast than the best-performing method ([Formula: see text], [Formula: see text], [Formula: see text]). Furthermore, our method is highly efficient. It takes 0.92 s to process a 720P color image on an Intel i7-6700 CPU. The objective results derive from the chi-square test indicate that subjects hold more positive attitudes toward our detail-enhanced images than the original X-ray images ([Formula: see text], [Formula: see text], [Formula: see text]). Conclusion We have conducted extensive experiments to evaluate the proposed image detail enhancement method. It can be concluded that (1) our method could significantly improve the visibility of the X-ray images. (2) our method is fast and effective, thus facilitating real applications.

Source: Digital Health

DOI: 10.1177/20552076231200981

Authors: Wenyan Bian, Yang Yang

Institutions: Jiangsu University, Affiliated Hospital of Jiangsu University

Language: English

Topic: Image Enhancement Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,500