Laser & Optoelectronics Progress, Volume. 60, Issue 16, 1610006(2023)
Optimization of Hyperspectral Image Denoising Based on Local Truncated Nuclear Norm
Fig. 1. SSIM under different trancated values t
Fig. 2. PSNR under different truncated values t
Fig. 3. SSIM under different expected rank r
Fig. 4. PSNR under different expected rank r
Fig. 5. SSIM of the algorithm before and after improvement
Fig. 6. PSNR of the algorithm before and after improvement
Fig. 7. Denoising results of each denoising method in band 2 of Pavia University dataset. (a) Original image; (b) TNN-LLRGTV; (c) LLRGTV; (d) LRTDTV; (e) LRMR; (f) NAILRMA
Fig. 8. Denoising results of each denoising method in band 110 of Salinas dataset. (a) Original image; (b) TNN-LLRGTV; (c) LLRGTV; (d) LRTDTV; (e) LRMR; (f) NAILRMA
Fig. 9. Comparison of spectral curves of different denoising algorithms
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Haichen Wang, Shengqi Wang, Xueyou Hu. Optimization of Hyperspectral Image Denoising Based on Local Truncated Nuclear Norm[J]. Laser & Optoelectronics Progress, 2023, 60(16): 1610006
Category: Image Processing
Received: Aug. 12, 2022
Accepted: Oct. 13, 2022
Published Online: Aug. 15, 2023
The Author Email: Hu Xueyou (xueyouhu@hfuu.edu.cn)