Self-supervised blind denoising for Poisson-Gaussian noise remains a challenging task. Pseudo-supervised pairs constructed from single noisy images re-corrupt the signal and degrade the performance. The visible blindspots solve the information loss in masked inputs. However, without explicitly noise sensing, mean square error as an objective function cannot adjust denoising intensities for dynamic noise levels, leading to noticeable residual noise. In this paper, we propose Blind2Sound, a simple yet effective approach to overcome residual noise in denoised images. The proposed adaptive re-visible loss senses noise levels and performs personalized denoising without noise residues while retaining the signal lossless. The theoretical analysis of intermediate medium gradients guarantees stable training, while the Cramer Gaussian loss acts as a regularization to facilitate the accurate perception of noise levels and improve the performance of the denoiser. Experiments on synthetic and real-world datasets show the superior performance of our method, especially for single-channel images.

翻译：对于泊松-高斯噪声的自监督盲去噪仍是一项具有挑战性的任务。从单张含噪图像构造的伪监督对会重新破坏信号并降低性能。可见盲点解决了掩码输入中的信息丢失问题。然而，在没有显式噪声感知的情况下，均方误差作为目标函数无法针对动态噪声水平调整去噪强度，导致出现明显的残余噪声。本文提出Blind2Sound——一种简单而有效的克服去噪图像中残差噪声的方法。所提出的自适应重可见损失能够感知噪声水平，执行无噪声残留的个性化去噪，同时保持信号无损失。对中间介质梯度的理论分析保证了训练的稳定性，而克拉默高斯损失作为正则化项，有助于精确感知噪声水平并提升去噪器的性能。在合成和真实数据集上的实验表明，我们的方法具有优越性能，尤其适用于单通道图像。