  Although the advances of self-supervised blind denoising are significantly
superior to conventional approaches without clean supervision in synthetic
noise scenarios, it shows poor quality in real-world images due to spatially
correlated noise corruption. Recently, pixel-shuffle downsampling (PD) has been
proposed to eliminate the spatial correlation of noise. A study combining a
blind spot network (BSN) and asymmetric PD (AP) successfully demonstrated that
self-supervised blind denoising is applicable to real-world noisy images.
However, PD-based inference may degrade texture details in the testing phase
because high-frequency details (e.g., edges) are destroyed in the downsampled
images. To avoid such an issue, we propose self-residual learning without the
PD process to maintain texture information. We also propose an order-variant PD
constraint, noise prior loss, and an efficient inference scheme (progressive
random-replacing refinement ($\text{PR}^3$)) to boost overall performance. The
results of extensive experiments show that the proposed method outperforms
state-of-the-art self-supervised blind denoising approaches, including several
supervised learning methods, in terms of PSNR, SSIM, LPIPS, and DISTS in
real-world sRGB images.
