吉林大学学报（理学版）2024，Vol.62Issue(1)：106-115,10.DOI:10.13413/j.cnki.jdxblxb.2022479
基于NSST与稀疏先验的遥感图像去模糊方法
Remote Sensing Image Deblurring Method Based on NSST and Sparse Prior
摘要
Abstract
Aiming at the blurring problem of remote sensing images,we designed an image restoration algorithm based on non-subsampled shearlet transformation and sparse prior.Firstly,the image recovery model was created by setting the sparse a priori condition of remote sensing image under non-subsampled shearlet decomposition of the high-frequency image.Secondly,the model was solved by using the alternating direction multiplier method.Thirdly,the high-frequency image was restricted by the soft thresholding method,and the guided filtering was conducted in the low-frequency image to maintain the detailed information of the image as much as possible.Finally,the high-frequency image and the low-frequency image were reconstructed,the reconstructed image was subjected to deep denoising by using convolutional neural networks,ultimately restoring a clear image.The deblurring algorithm was compared with H-PNP,GSR,and L2TV algorithms through experiments.The experimental results show that the algorithm can effectively remove blurring and noise in remote sensing images,preserve the edge details of the image,and the objective evaluation indexes are higher than the other three comparative experimental algorithms.关键词
遥感图像/非下采样剪切波变换/稀疏先验/图像去模糊/交替方向乘子法Key words
remote sensing image/non-subsampled shearlet transformation/sparse prior/image deblurring/alternating direction multiplier method分类
信息技术与安全科学引用本文复制引用
成丽波,董伦,李喆,贾小宁..基于NSST与稀疏先验的遥感图像去模糊方法[J].吉林大学学报（理学版）,2024,62(1):106-115,10.基金项目
国家自然科学基金(批准号:12171054)和吉林省教育厅科学技术研究项目(批准号:JJKH20230788KJ). （批准号:12171054）
