Abstract:
Natural image denoising priors include non-local self similarity (NSS). Most denoising methods use NSS patches. This paper introduces a pixel-level NSS prior to search similar pixels across a non-local region.
Finding closely similar pixels is easier than finding similar patches in natural images, which can improve image denoising.
We propose an accurate noise level estimation method using the pixel-level NSS prior and then develop a lifting Haar transform and Wiener filtering blind image denoising method.
On benchmark datasets, the proposed method outperforms non-deep methods and is competitive with state-of-the-art deep learning methods for real-world image denoising.
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