  Recently, significant progress has been made on image denoising with strong
supervision from large-scale datasets. However, obtaining well-aligned
noisy-clean training image pairs for each specific scenario is complicated and
costly in practice. Consequently, applying a conventional supervised denoising
network on in-the-wild noisy inputs is not straightforward. Although several
studies have challenged this problem without strong supervision, they rely on
less practical assumptions and cannot be applied to practical situations
directly. To address the aforementioned challenges, we propose a novel and
powerful self-supervised denoising method called CVF-SID based on a Cyclic
multi-Variate Function (CVF) module and a self-supervised image disentangling
(SID) framework. The CVF module can output multiple decomposed variables of the
input and take a combination of the outputs back as an input in a cyclic
manner. Our CVF-SID can disentangle a clean image and noise maps from the input
by leveraging various self-supervised loss terms. Unlike several methods that
only consider the signal-independent noise models, we also deal with
signal-dependent noise components for real-world applications. Furthermore, we
do not rely on any prior assumptions about the underlying noise distribution,
making CVF-SID more generalizable toward realistic noise. Extensive experiments
on real-world datasets show that CVF-SID achieves state-of-the-art
self-supervised image denoising performance and is comparable to other existing
approaches. The code is publicly available from
https://github.com/Reyhanehne/CVF-SID_PyTorch .
