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One of the fundamental challenges in image restoration is denoising, wherethe objective is to estimate the clean image from its noisy measurements. Totackle such an ill-posed inverse problem, the existing denoising approachesgenerally focus on exploiting effective natural image priors. The utilizationand analysis of the noise model are often ignored, although the noise model canprovide complementary information to the denoising algorithms. In this paper,we propose a novel Flow-based joint Image and NOise model (FINO) thatdistinctly decouples the image and noise in the latent space and losslesslyreconstructs them via a series of invertible transformations. We furtherpresent a variable swapping strategy to align structural information in imagesand a noise correlation matrix to constrain the noise based on spatiallyminimized correlation information. Experimental results demonstrate FINO'scapacity to remove both synthetic additive white Gaussian noise (AWGN) and realnoise. Furthermore, the generalization of FINO to the removal of spatiallyvariant noise and noise with inaccurate estimation surpasses that of thepopular and state-of-the-art methods by large margins.

FINO: Flow-based Joint Image and Noise Model