Application of Multiple-Optimization Filtering Algorithm in Remote Sensing Image Denoising

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 18; p. 7813
• Main Authors: Zhang, Xuelin, Li, Yuan, Feng, Xiang, Hua, Jian, Yue, Dong, Wang, Jianxiong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023; MDPI
• Subjects: Algorithms; bilateral filtering; differential evolution algorithm; edge detection operator; Gaussian noise; Image retrieval; Optimization algorithms; Remote sensing; remote sensing imagery; Standard deviation; Wavelet transforms
• DOI: 10.3390/s23187813
• ISSN: 1424-8220

Denoising remote sensing images is crucial in the application and research of remote sensing imagery. Noise in remote sensing images originates from sensor characteristics, signal transmission, and environmental conditions, among which Gaussian noise is the most common type. In this paper, we proposed a multiple-optimization bilateral filtering (MOBF) algorithm based on edge detection and differential evolution (DE) methods. The proposed algorithm optimizes the spatial domain filtering kernel and the spatial domain Gaussian kernel by using the standard deviation and width of the edge response. By employing the DE algorithm, the individuals in the population based on the standard deviation of the gray value domain are subjected to iterative mutation, crossover, and selection operations to refine the latent solution vectors and determine the optimal color space for optimizing the standard deviation of the pixel range domain kernel. As a result, the MOBF algorithm, which does not require any parameter input, is realized. To verify the feasibility and effectiveness of the proposed algorithm, denoising experiments were conducted on remote sensing images by using evaluation metrics such as the mean squared error, peak signal-to-noise ratio, and structural similarity index. The experimental results revealed that the MOBF algorithm outperforms traditional algorithms for all three evaluation metrics.