Blind signal separation and identification of mixtures of images

Blind Image Deblurring based on Kernel Mixture

Blind Image deblurring tries to estimate blurriness and a latent image out of a blurred image. This estimation, as being an ill-posed problem, requires imposing restrictions on the latent image or a blur kernel that represents blurriness. Different from recent studies that impose some priors on the latent image, this paper regulates the structure of the blur kernel. We propose a kernel mixture structure while using the Gaussian kernel as a base kernel. By combining multiple Gaussian kernels structurally enhanced in terms of scales and centers, the kernel mixture becomes capable of modeling nea…

基于稀疏性的盲源分离

Blind Source Separation Based on Sparsity

本报告探讨了基于稀疏性的盲源分离方法，介绍了MCA理论及MMCA、GMCA等变体，提出了基于块稀疏字典学习的改进算法SAC+BK-SVD，实验证明其提升了盲图像分离质量。

增强盲源分离的解离主成分分析

Enhancing Blind Source Separation with Dissociative Principal Component Analysis

提出解离主成分分析（DPCA），通过联合估计主成分和载荷向量并显式建模其相互依赖关系，克服传统稀疏PCA在源重叠时性能下降的问题，在模拟fMRI源恢复、前景背景分离等任务中优于经典sPCA。

WM-MoE：用于盲恶劣天气去除的天气感知多尺度混合专家模型

WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removal

针对未知类型、强度及混合天气的盲去除问题，提出基于Transformer的WM-MoE，通过WEAR、WGF-CL和多尺度专家实现自适应天气建模，并在多个数据集及下游分割中取得最优性能。

Blind stain separation using model-aware generative learning and its applications on fluorescence microscopy images

Multiple stains are usually used to highlight biological substances in biomedical image analysis. To decompose multiple stains for co-localization quantification, blind source separation is usually performed. Prior model-based stain separation methods usually rely on stains' spatial distributions over an image and may fail to solve the co-localization problem. With the advantage of machine learning, deep generative models are used for this purpose. Since prior knowledge of imaging models is ignored in purely data-driven solutions, these methods may be sub-optimal. In this study, a novel learni…