IEEE Trans Image Process - Practical signal-dependent noise parameter estimation from a single noisy image.

Tópicos

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Resumo

The additive white Gaussian noise is widely assumed in many image processing algorithms. However, in the real world, the noise from actual cameras is better modeled as signal-dependent noise (SDN). In this paper, we focus on the SDN model and propose an algorithm to automatically estimate its parameters from a single noisy image. The proposed algorithm identifies the noise level function of signal-dependent noise assuming the generalized signal-dependent noise model and is also applicable to the Poisson-Gaussian noise model. The accuracy is achieved by improved estimation of local mean and local noise variance from the selected low-rank patches. We evaluate the proposed algorithm with both synthetic and real noisy images. Experiments demonstrate that the proposed estimation algorithm outperforms the state-of-the-art methods.

Resumo Limpo

addit white gaussian nois wide assum mani imag process algorithm howev real world nois actual camera better model signaldepend nois sdn paper focus sdn model propos algorithm automat estim paramet singl noisi imag propos algorithm identifi nois level function signaldepend nois assum general signaldepend nois model also applic poissongaussian nois model accuraci achiev improv estim local mean local nois varianc select lowrank patch evalu propos algorithm synthet real noisi imag experi demonstr propos estim algorithm outperform stateoftheart method

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