Universal Denoising of Continuous Amplitude Signals with Applications to Images

We consider the problem of image denoising wherein the statistical characterization of the noise corruption mechanism is known. We make no assumptions on the nature or statistics of the underlying noise-free signal. A denoiser is proposed which, although ignorant of the statistical properties of the noise-free image, does essentially as well as a scheme with full knowledge of the statistics. The solution is presented as a sequence of schemes that progressively consider larger neighborhoods (contexts) around a pixel being denoised, to achieve optimum performance under a user-defined distortion measure.

• Type: Conference
• Year: 2006
• Where: ICIP
• Authors: Kamakshi Sivaramakrishnan, Tsachy Weissman
• Keywords: ICIP 2006, Image Processing, Larger Neighborhoods, Noise Corruption Mechanism, Statistical Characterization, User-defined Distortion Measure