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- W2016150650 abstract "State-of-the-art image denoising algorithms usually assume additive white Gaussian noise (AWGN), although they have achieved outstanding performance, modeling and removing real signal dependent noise from a single image still remains a challenging problem. In this paper we propose a segmentation-based image denoising algorithm for signal dependent noise. Incorporating a noise identification algorithm, we integrate these two modules into a full blind, end-to-end denoising algorithm for signal dependent noise. First, we identify the noise level function for a given single noisy image. Then, after initial denoising, segmentation is applied to the pre-filtered image. Assuming the noise level of each segment is constant, we apply AWGN denoising algorithm to each segment. We obtain a final de-noised image by composing the denoised segments. Various experimental results on synthetic and real noisy images show that our algorithm outperforms state-of-the-art denoising algorithms in removing real signal dependent noise." @default.
- W2016150650 created "2016-06-24" @default.
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- W2016150650 date "2014-10-01" @default.
- W2016150650 modified "2023-10-12" @default.
- W2016150650 title "Signal dependent noise removal from a single image" @default.
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- W2016150650 doi "https://doi.org/10.1109/icip.2014.7025542" @default.
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