4. FSIB结构: Fuse Block 二. 表达: 1. For PSNR-oriented model, both pixel-level and frequency-level loss functions are adopted to guide the learning of the network.
The mutual fusion of the two can better model the interrelationship between HSI and RGB image, thereby achieving better SSR performance. In FSDFF, we design a frequency domain feature learning branch (FDFL) and a spatial domain feature learning branch (SDFL) to learn the frequency and spatial ...
This would make it possible, for example, to determine the mutual error contribution in the simultaneous determination of the optical properties and the 3D topography of turbid samples or to enable new possibilities for data evaluation. The first progress with a similar approach for the corrected ...
Mutual information-based selection of optimal spatial–temporal patterns for single-trial EEG-based BCIs. Pattern Recognit. 2012, 45, 2137–2144. [Google Scholar] [CrossRef] Zhang, Y.; Wang, Y.; Jin, J.; Wang, X. Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based...