An Image Enhancement Method Based on Partial Differential Equations to Improve Dark Channel Theory. Issue 4 (May 2021)

• Record Type: Journal Article
• Authors: Li, Pengcheng; Gu, Xiaohui
• Is Part Of: IOP conference series. Volume 769:Issue 4(2021)
• Publication Date: 2021-05
• Subjects: Image Enhancement -- Edge Preserving -- Dark Channel Prior -- Partial Differential Equation; Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/769/4/042112
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: Image applications appears in all aspects of life, such as medical imaging, electronic monitoring, face recognition, aerial photography remote sensing, fingerprint analysis, robot vision, automatic driving, intelligent car and so on in the civil field, as well as tanks, armored vehicles, artillery, missiles, bat submunitions in the military field. However, in the process of image acquisition or storage and transportation, the image quality will decline due to environmental conditions, equipment conditions or transmission compression, and so on. In order to accurately analyze the image, we need to carry out enhancement processing. In this paper, the current research status of image enhancement methods is analyzed. The traditional dark channel prior algorithm has some problems, such as edge or texture blur and so on. The processing effect is not ideal. Considering the limitations of the traditional dark channel prior algorithm, this paper proposes a improved algorithms based on the dark channel prior by combining partial differential equation. The main work includes the following aspects. First, the partial differential equation algorithm is combined with the dark channel prior method, and the improved algorithm is applied to enhance the image with fog. The contrast experiment of enhancement effect and running time is designed to verify the image enhancement effect of the improved algorithm. Second, the improved algorithms proposed in this paper are compared with traditional algorithms. After making the subjective and objective evaluation, it is found that the improved method in this paper has better image processing effect, stronger fog and noise removal effect, better edge retention effect, more information acquisition, and higher computational efficiency.