Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FINO: Flow-based Joint Image and Noise Model   One of the fundamental challenges in image restoration is denoising, where
the objective is to estimate the clean image from its noisy measurements. To
tackle such an ill-posed inverse problem, the existing denoising approaches
generally focus on exploiting effective natural image priors. The utilization
and analysis of the noise model are often ignored, although the noise model can
provide complementary information to the denoising algorithms. In this paper,
we propose a novel Flow-based joint Image and NOise model (FINO) that
distinctly decouples the image and noise in the latent space and losslessly
reconstructs them via a series of invertible transformations. We further
present a variable swapping strategy to align structural information in images
and a noise correlation matrix to constrain the noise based on spatially
minimized correlation information. Experimental results demonstrate FINO's
capacity to remove both synthetic additive white Gaussian noise (AWGN) and real
noise. Furthermore, the generalization of FINO to the removal of spatially
variant noise and noise with inaccurate estimation surpasses that of the
popular and state-of-the-art methods by large margins.

26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat