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

2006 ◽  
Author(s):  
L. Tessens ◽  
A. Pižurica ◽  
A. Alecu ◽  
A. Munteanu ◽  
W. Philips
Keyword(s):  
Image Denoising ◽  
Image Statistics ◽  
Spatially Adaptive

Spatially adaptive context-based wavelet shrinkage for borescope image denoising

2006 ◽  
Author(s):  
Peng Ding ◽  
Qi Shuang Ma ◽  
Chang You Li ◽  
Hong Yu Yao
Keyword(s):  
Image Denoising ◽  
Wavelet Shrinkage ◽  
Spatially Adaptive

Image Denoising Algorithm via Spatially Adaptive Bilateral Filtering

2013 ◽  
Vol 760-762 ◽  
pp. 1515-1518 ◽  
Author(s):  
Min Qi ◽  
Zuo Feng Zhou ◽  
Jing Liu ◽  
Jian Zhong Cao ◽  
Hao Wang ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Image Denoising ◽  
Spatial Information ◽  
Bilateral Filtering ◽  
Denoising Method ◽  
Local Statistics ◽  
Filtering Algorithm ◽  
Spatially Adaptive ◽  
Intensity Information ◽  
Noise Experiment

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.

scholarly journals Image Denoising Using Nonlocal Means with Shape-Adaptive Patches and New Weights

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chenglin Zuo ◽  
Jun Ma ◽  
Hao Xiong ◽  
Lin Ran
Keyword(s):  
Image Denoising ◽  
Image Sensors ◽  
Initial Estimate ◽  
Nonlocal Means ◽  
Spatially Adaptive ◽  
Shape Adaptation ◽  
Residual Image ◽  
Method Noise ◽  
Ccd Image

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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