Abstract
The inherent physical limitations of imaging sensors lead to prevalence of additive white Gaussian noise in images which deters the feature extraction and analysis. There exists a number of denoising algorithms in literature, demonstrating their efficacy for removing noise while preserving feature details. At the crossing of functional and statistical analysis, one argues with new methods being devised quite frequently, whether the decade old BM3D is still efficient or not. While carrying out extended experimentation and evaluation for removal of Gaussian noise from natural images in terms peak signal to noise ratio, an argument in favor of BM3D has been presented in this manuscript.
Highlights
The digital data display and transmission has significantly propelled major fields of application such remote sensing, medical sciences, astronomy, surveillance and computer vision
The mechanism of image acquisition embodies the basic principle of illumination and projection of the object under investigation
Often objects are illuminated with inconsistent photon count. These factors results in the manifestation of additive white Gaussian noise in the image which irrevocably destroys the quality of image and hinders image interpretability (Zhang et al, 2012)
Summary
The digital data display and transmission has significantly propelled major fields of application such remote sensing, medical sciences, astronomy, surveillance and computer vision. The progression in the fields of digital signal processing, statistical methods and mathematical theories has resulted in the coining of technical algorithms for removal of noise. The efficacy of an image denoising algorithm is defined by noise removal amount while preservation of information pixel detail.
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