Abstract
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.
Highlights
Digital imaging devices such as digital cameras and camera phones are ubiquitous in our daily life, which use complementary metal oxide semiconductors (CMOS) or charged coupled devices (CCD) image sensors to acquire images
Since the CMOS and CCD image sensors are subject to noise from two notable sources, i.e., electronic instruments and the photo-sensing devices [1, 2], the quality of captured images is usually not satisfactory, especially when images are taken in low light condition, which leads to degraded imaging results
BayesShrink is an adaptive, data-driven thresholding strategy via soft-thresholding which derives the threshold in a Bayesian framework, assuming a generalized Gaussian distribution for the wavelet coefficients. is method is adaptive to each sub-band because it depends on data-driven estimates of the parameters. e threshold for a given sub-band is derived by minimizing Bayesian risk as follows: T
Summary
Digital imaging devices such as digital cameras and camera phones are ubiquitous in our daily life, which use complementary metal oxide semiconductors (CMOS) or charged coupled devices (CCD) image sensors to acquire images. In [20, 21], Buades et al first proposed the nonlocal principle-based denoising method, called nonlocal means (NLM). In this method, noise-free pixel is estimated as a weighted average of all pixels in the image, where the weights are determined based on the similarity between the patch centered at the pixel being estimated and the patches centered at other pixels. NLM and its extensions have achieved significant denoising results, only exploiting the spatially nonlocal redundancy still limits their performance. En, the preserved residual image is combined with initial estimate to obtain a basic denoising result, based on which spatially adaptive patch shapes are defined using LPA-ICI and new weights are calculated.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
