Deep learning-based denoiser has been the focus of recent development on
image denoising. In the past few years, there has been increasing interest in
developing self-supervised denoising networks that only require noisy images,
without the need for clean ground truth for training. However, a performance
gap remains between current self-supervised methods and their supervised
counterparts. Additionally, these methods commonly depend on assumptions about
noise characteristics, thereby constraining their applicability in real-world
scenarios. Inspired by the properties of the Frobenius norm expansion, we
discover that incorporating a trace term reduces the optimization goal
disparity between self-supervised and supervised methods, thereby enhancing the
performance of self-supervised learning. To exploit this insight, we propose a
trace-constraint loss function and design the low-trace adaptation Noise2Noise
(LoTA-N2N) model that bridges the gap between self-supervised and supervised
learning. Furthermore, we have discovered that several existing self-supervised
denoising frameworks naturally fall within the proposed trace-constraint loss
as subcases. Extensive experiments conducted on natural and confocal image
datasets indicate that our method achieves state-of-the-art performance within
the realm of zero-shot self-supervised image denoising approaches, without
relying on any assumptions regarding the noise.

基于深度学习的去噪器是图像去噪领域最近发展的焦点，本文提出了一种基于追踪约束损失函数和 LoTA-N2N 模型的方法来弥合无监督学习和监督学习之间的差距，并在自然图像和共焦图像数据集上取得了最新的零样本自我监督图像去噪方法的优秀表现，无需依赖于任何对噪声的假设。

低追踪适应的零样本自监督盲图像去噪

Low-Trace Adaptation of Zero-shot Self-supervised Blind Image Denoising

Most unsupervised denoising methods are based on the assumption that imaging
noise is either pixel-independent, i.e., spatially uncorrelated, or
signal-independent, i.e., purely additive. However, in practice many imaging
setups, especially in microscopy, suffer from a combination of signal-dependent
noise (e.g. Poisson shot noise) and axis-aligned correlated noise (e.g. stripe
shaped scanning or readout artifacts). In this paper, we present the first
unsupervised deep learning-based denoiser that can remove this type of noise
without access to any clean images or a noise model. Unlike self-supervised
techniques, our method does not rely on removing pixels by masking or
subsampling so can utilize all available information. We implement a
Variational Autoencoder (VAE) with a specially designed autoregressive decoder
capable of modelling the noise component of an image but incapable of
independently modelling the underlying clean signal component. As a
consequence, our VAE's encoder learns to encode only underlying clean signal
content and to discard imaging noise. We also propose an additional decoder for
mapping the encoder's latent variables back into image space, thereby sampling
denoised images. Experimental results demonstrate that our approach surpasses
existing methods for self- and unsupervised image denoising while being robust
with respect to the size of the autoregressive receptive field. Code for this
project can be found at this https URL

本研究介绍了一种基于深度学习的无监督降噪方法，能够移除图像中的信号相关噪声和轴向相关噪声，无需干净图像或噪声模型作为输入，实验结果表明该方法优于现有的自监督和无监督图像降噪方法。