5 papers
An ICTM-RMSAV Framework for Bias-Field Aware Image Segmentation under Poisson and Multiplicative Noise
Xinyu Wang, Wenjun Yao, Fanghui Song +1
Image segmentation is a core task in image processing, yet many methods degrade when images are heavily corrupted by noise and exhibit intensity inhomogeneity. Within the iterative…
TA-LSDiff:Topology-Aware Diffusion Guided by a Level Set Energy for Pancreas Segmentation
Yue Gou, Fanghui Song, Yuming Xing +3
Pancreas segmentation in medical image processing is a persistent challenge due to its small size, low contrast against adjacent tissues, and significant topological variations. Tr…
Progressive Alignment Degradation Learning for Pansharpening
Enzhe Zhao, Zhichang Guo, Yao Li +2
Deep learning-based pansharpening has been shown to effectively generate high-resolution multispectral (HRMS) images. To create supervised ground-truth HRMS images, synthetic data…
A class of thin-film equations with space-time dependent gradient nonlinearity and its application to image sharpening
Yuhang Li, Zhichang Guo, Fanghui Song +2
We introduce a class of thin-film equations with space-time dependent gradient nonlinearity and apply them to image sharpening. By modifying the potential well method, we overcome…
Mixed geometry information regularization for image multiplicative denoising
Shengkun Yang, Zhichang Guo, Jia Li +2
This paper focuses on solving the multiplicative gamma denoising problem via a variation model. Variation-based regularization models have been extensively employed in a variety of…