Spatially adaptive image denoising based on joint image statistics in the curvelet domain

Author(s):  
L. Tessens ◽  
A. Pižurica ◽  
A. Alecu ◽  
A. Munteanu ◽  
W. Philips
2006 ◽  
Author(s):  
Peng Ding ◽  
Qi Shuang Ma ◽  
Chang You Li ◽  
Hong Yu Yao

2013 ◽  
Vol 760-762 ◽  
pp. 1515-1518 ◽  
Author(s):  
Min Qi ◽  
Zuo Feng Zhou ◽  
Jing Liu ◽  
Jian Zhong Cao ◽  
Hao Wang ◽  
...  

The classical bilateral filtering algorithm is a non-linear and non-iterative image denoising method in spatial domain which utilizes the spatial information and the intensity information between a point and its neighbors to smooth the noisy images while preserving edges well. To further improve the image denoising performance, a spatially adaptive bilateral filtering image deonoising algorithm with low computational complexity is proposed. The proposed algorithm takes advantage of the local statistics characteristic of the image signal to better preserve the edges or textures while suppressing the noise. Experiment results show that the proposed image denoising algorithm achieves better performance than the classical bilateral filtering image denoising method.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chenglin Zuo ◽  
Jun Ma ◽  
Hao Xiong ◽  
Lin Ran

Digital images captured from CMOS/CCD image sensors are prone to noise due to inherent electronic fluctuations and low photon count. To efficiently reduce the noise in the image, a novel image denoising strategy is proposed, which exploits both nonlocal self-similarity and local shape adaptation. With wavelet thresholding, the residual image in method noise, derived from the initial estimate using nonlocal means (NLM), is exploited further. By incorporating the role of both the initial estimate and the residual image, spatially adaptive patch shapes are defined, and new weights are calculated, which thus results in better denoising performance for NLM. Experimental results demonstrate that our proposed method significantly outperforms original NLM and achieves competitive denoising performance compared with state-of-the-art denoising methods.


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