원문정보
초록
영어
For hyperspectral remote sensing image denoising, this paper proposed image denoising based on non-local low-rank dictionary learning. The basic idea of algorithm is to use strong relativity of all wave bands of hyperspectral remote sensing image with local self-similarity and local sparsity of image to improve the denoising performance. First of all, combined with the strong relativity, non-local self-similarity and local sparsity, non-local low-rank dictionary learning is established. Then iterative method is used to solve the model to get redundant dictionary and sparsity to represent coefficient. Finally, redundant dictionary and sparsity is used to express restored image of coefficient. Compared with the existing advanced algorithm, by making full use of strong relativity each band of hyperspectral image, it makes the algorithm obtain the information on details to well keep the hyperspectral remote sensing image, to improve the visual effect. Experimental results verify the effectiveness of the algorithm in this paper.
목차
1. Introduction
2. Dictionary Learning
3. Image Denoising Algorithm Based on Non-Local Low-Rank Dictionary Learning
3.1. Ono-Local Low-Rank Dictionary Learning Model
3.2. Solving of the Nonlocal Low-Rank Dictionary Model
3.3. Hyperspectral Remote Sensing Image Denoising
4. Detailed Steps and Analysis of the Algorithm
4.1. Detailed Steps of the Algorithm
4.2. Analysis of the Algorithm Complexity
5. Experiment Results and Analysis
6. Conclusion
References