We present an extension of the Discrete Universal DEnoiser (DUDE) specialized for the denoising of grayscale images. The original DUDE is a low- complexity algorithm aimed at recovering discrete sequences corrupted by discrete memoryless noise of known statistical characteristics. It is universal, in the sense of asymptotically achieving, without access to any information on the statistics of the clean sequence, the same performance as the best denoiser that does have access to such information. The denoising performance of the DUDE, however, is poor on grayscale images of practical size. The difficulty lies in the fact that one of the DUDE ga ss key components is the determination of conditional empirical probability distributions of image samples, given the sample values in their neighborhood. When the alphabet is moderately large (as is the case with grayscale images), even for a small-sized neighborhood, the required distributions would be estimated from a large collection of sparse statistics, resulting in poor estimates that would cause the algorithm to fall significantly short of the asymptotically optimal performance. The present work enhances the basic DUDE scheme by incorporating statistical modeling tools that have proven successful in addressing similar issues in lossless image compression. The enhanced framework is tested on additive and non-additive noise, and shown to yield powerful denoisers that significantly surpass the state of the art in the case of non-additive noise, and perform well for Gaussian noise.
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