Conventional denoising and noise level estimation typically require data redundancy from multiple measurements or prior assumptions, such as smooth image prior or similarity between image patches. Here, we propose a single image denoising algorithm with noise map estimation by identifying the noise-only principle components based on universal properties of random covariance matrices, with the data redundancy created by segmenting data in the Fourier or wavelet domains. The proposed method is applicable to medical and other imaging modalities with spatially-varying noise, and is particularly beneficial to quantitative MRI acquisitions with a limited number of scans.
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