Algorithm of remote sensing image matching based on corner-point

Changjie, Wang; Hua, Nian

doi:10.1109/rsip.2017.7958803

Cited by 6 publications

(1 citation statement)

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“…To compare and analyze the existing noise removal techniques, the denoising results of BTV, SB-ITV, SKNLM, and the proposed algorithm were compared with the original and noisy images. For objective evaluation, the performance was analyzed using the PSNR and SSIM of the denoised result images [14,15].…”

Section: A Experimental Settingmentioning

confidence: 99%

A Modified Steering Kernel Filter for AWGN Removal based on Kernel Similarity

Cheon

Kim

2022

JICCE

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Noise generated during image acquisition and transmission can negatively impact the results of image processing applications, and noise removal is typically a part of image preprocessing. Denoising techniques combined with nonlocal techniques have received significant attention in recent years, owing to the development of sophisticated hardware and image processing algorithms, much attention has been paid to; however, this approach is relatively poor for edge preservation of fine image details. To address this limitation, the current study combined a steering kernel technique with adaptive masks that can adjust the size according to the noise intensity of an image. The algorithm sets the steering weight based on a similarity comparison, allowing it to respond to edge components more effectively. The proposed algorithm was compared with existing denoising algorithms using quantitative evaluation and enlarged images. The proposed algorithm exhibited good general denoising performance and better performance in edge area processing than existing non-local techniques.

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Section: A Experimental Settingmentioning

confidence: 99%