TM-BSN: Triangular-Masked Blind-Spot Network for Real-World Self-Supervised Image Denoising
Blind-spot networks (BSNs) enable self-supervised image denoising by preventing access to the target pixel, allowing clean signal estimation without ground-truth supervision. However, this approach assumes pixel-wise noise independence, which is violated in real-world sRGB images due to spatially correlated noise from the camera's image signal processing (ISP) pipeline. While several methods employ downsampling to decorrelate noise, they alter noise statistics and limit the network's ability to utilize full contextual information. In this paper, we propose the Triangular-Masked Blind-Spot Network (TM-BSN), a novel blind-spot architecture that accurately models the spatial correlation of r...
| Authors | Junyoung Park, Youngjin Oh, Nam Ik Cho |
|---|---|
| Primary category | eess.IV |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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