SVB: Self-Supervised Real CT Denoising via Similarity-Based Visual Blind-Spot Scheme

Low-dose and photon-counting computed tomography (CT) denoising is a challenging task in medical imaging that has attracted significant attention. The supervised deep denoising method has significant effects, but requires a large amount of noisy-clean image pairs, which are often not available in practice. In this article, we develop a novel self-supervised method via similarity-based visual invisible-spot scheme (SVB) for real CT image denoising. First, a suitable paired similar images is constructed by analyzing the characteristics between similar images at different intervals, which relaxes the condition of noise independence between them. Second, we introduce the invisible-spot scheme into similarity-based self-supervised methods, associating invisible-spot denoising with original noisy image denoising to eliminate independent noise and avoid the loss of valuable pixel information. Third, to improve the generalization of the network, we design a denoising network architecture that can estimate unknown noise levels. Experimentally, the SVB method is superior to supervised methods and other self-supervised methods to a large extent, and has excellent denoising performance while ensuring the restoration of intrinsic features.