Self2Grad: Self-Supervised Detail-Preserving Denoising of Single Noisy Images
Obtaining large-scale noisy/clean image pairs from the real world to train a denoising model is a difficult and cumbersome task. Self-supervised image denoisers have gained recent attention by adopting blind-spot networks to remove noise from single noisy images. However, the results of blind-spot denoisers suffer from losses of image detail caused by the selection of random masks. Here, we propose an edge-enhanced denoising approach called Self2Grad that compensates for the loss of important details when using self-supervised networks. Specifically, we develop a gradient-regularized loss function, which when added to the overall network loss acts to preserve details in images processed by blind-spot networks. In addition, we show that the loss obtained with Self2Grad is approximately equal to the loss incurred by supervised approaches. When compared to state-of-the-art (SOTA) blind-spot denoisers, Self2Grad delivers superior performance on both synthetic and real-world datasets. Indeed, Self2Grad attains comparable denoising results as methods that operate on multiple images. In particular, Self2Grad is especially effective on images corrupted by high levels of noise.
the National Natural Science Foundation of China(Nos. 62176036, 62102059)
the Natural Science Foundation of Liaoning Province (Nos. 2023-MS-126, 2023JH6/100100072)
the Fundamental Research Funds for the Central Universities (No. 3132023523).
Email Address of Submitting Authorbolin_song@dlmu.edu.cn
ORCID of Submitting Author0009-0005-6108-4695
Submitting Author's InstitutionDalian Maritime University
Submitting Author's Country