Discontinuous Galerkin Neural Operator for Pathology Defocus Deblurring
Defocus deblurring in pathological microscopy remains challenging due to the spatially varying and locally discontinuous nature of optical blur induced by a position-dependent integral imaging process. Existing deep learning methods, constrained by shift-invariance assumptions and limited interpretability, are not well suited to such heterogeneous blur patterns. Neural operators provide a principled alternative by modeling defocus formation directly as an integral operator, offering a new perspective on defocus deblurring. However, most existing neural operator architectures for low-level vision rely on globally parameterized kernels that assume smoothness and stationarity, limiting their...
| Authors | Shaoqing Duan, Haofei Song, Xintian Mao et al. |
|---|---|
| Primary category | eess.IV |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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