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