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Recently, significant progress has been made on image denoising with strongsupervision from large-scale datasets. However, obtaining well-alignednoisy-clean training image pairs for each specific scenario is complicated andcostly in practice. Consequently, applying a conventional supervised denoisingnetwork on in-the-wild noisy inputs is not straightforward. Although severalstudies have challenged this problem without strong supervision, they rely onless practical assumptions and cannot be applied to practical situationsdirectly. To address the aforementioned challenges, we propose a novel andpowerful self-supervised denoising method called CVF-SID based on a Cyclicmulti-Variate Function (CVF) module and a self-supervised image disentangling(SID) framework. The CVF module can output multiple decomposed variables of theinput and take a combination of the outputs back as an input in a cyclicmanner. Our CVF-SID can disentangle a clean image and noise maps from the inputby leveraging various self-supervised loss terms. Unlike several methods thatonly consider the signal-independent noise models, we also deal withsignal-dependent noise components for real-world applications. Furthermore, wedo not rely on any prior assumptions about the underlying noise distribution,making CVF-SID more generalizable toward realistic noise. Extensive experimentson real-world datasets show that CVF-SID achieves state-of-the-artself-supervised image denoising performance and is comparable to other existingapproaches. The code is publicly available fromhttps://github.com/Reyhanehne/CVF-SID_PyTorch .

CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image