June 2022 VOLUME 16 ISSUE 4 IJSTGY Special Issue on Deep Learning for High-Dimensional Sensing Editorial: Introduction to the Special Issue on Deep Learning for High-Dimensional Sensing High-Fidelity Hyperspectral Snapshot of Physical World: System Architecture, Dataset and Model A Deep Joint Network for Multispectral Demosaicking Based on Pseudo-Panchromatic Images Joint Spatial-Spectral Pattern Optimization and Hyperspectral Image Reconstruction Heterogeneity-Aware Recurrent Neural Network for Hyperspectral and Multispectral Image Fusion CIMFNet: Cross-Layer Interaction and Multiscale Fusion Network for Semantic Segmentation of High-Resolution Remote Sensing Images FRNet: Feature Reconstruction Network for RGB-D Indoor Scene Parsing Deep-Learning Supervised Snapshot Compressive Imaging Enabled by an End-to-End Adaptive Neural Network High-Speed Large-Scale Imaging Using Frame Decomposition From Intrinsic Multiplexing of Motion A Deep Learning Method for Optimal Undersampling Patterns and Image Recovery for MRI Exploiting Losses and Projections DEMO: A Flexible Deartifacting Module for Compressed Sensing MRI PUERT: Probabilistic Under-Sampling and Explicable Reconstruction Network for CS-MRI High-Throughput Deep Unfolding Network for Compressive Sensing MRI A Bayesian Based Deep Unrolling Algorithm for Single-Photon Lidar Systems Interpretable Deep Probabilistic Model for HRR Radar Signal and its Application to Target Recognition Learning to Detect Open Carry and Concealed Object With 77 GHz Radar (ISSN 1932-4553) 603-607 608-621 622-635 636-648 649-665 666-676 677-687 688-699 700-712 713-724 725-736 737-749 750-761 762-774 775-790 791-803 www.signalprocessingsociety.org [11] AUGUST 2022https://signalprocessingsociety.org/ https://dx.doi.org/10.1109/JSTSP.2022.3185190 https://ieeexplore.ieee.org/document/9829820 https://ieeexplore.ieee.org/document/9760181 https://dx.doi.org/10.1109/JSTSP.2022.3167891 https://ieeexplore.ieee.org/document/9770293 https://dx.doi.org/10.1109/JSTSP.2022.3172865 https://dx.doi.org/10.1109/JSTSP.2022.3179806 https://ieeexplore.ieee.org/document/9787797 https://ieeexplore.ieee.org/document/9790330 https://dx.doi.org/10.1109/JSTSP.2022.3180896 https://dx.doi.org/10.1109/JSTSP.2022.3180896 https://ieeexplore.ieee.org/document/9735276 https://dx.doi.org/10.1109/JSTSP.2022.3159032 https://dx.doi.org/10.1109/JSTSP.2022.3159032 https://ieeexplore.ieee.org/document/9774020 https://dx.doi.org/10.1109/JSTSP.2022.3174338 https://ieeexplore.ieee.org/document/9769913 https://dx.doi.org/10.1109/JSTSP.2022.3172592 https://dx.doi.org/10.1109/JSTSP.2022.3172592 https://ieeexplore.ieee.org/document/9749015 https://dx.doi.org/10.1109/JSTSP.2022.3164524 https://dx.doi.org/10.1109/JSTSP.2022.3164524 https://dx.doi.org/10.1109/JSTSP.2022.3171082 https://ieeexplore.ieee.org/document/9765323 https://dx.doi.org/10.1109/JSTSP.2022.3171082 https://dx.doi.org/10.1109/JSTSP.2022.3158057 https://ieeexplore.ieee.org/document/9732174 https://dx.doi.org/10.1109/JSTSP.2022.3170654 https://ieeexplore.ieee.org/document/9764377 https://ieeexplore.ieee.org/document/9763318 https://dx.doi.org/10.1109/JSTSP.2022.3170227 https://ieeexplore.ieee.org/document/9763322 https://dx.doi.org/10.1109/JSTSP.2022.3170228 https://ieeexplore.ieee.org/document/9737394 https://dx.doi.org/10.1109/JSTSP.2022.3160241 https://dx.doi.org/10.1109/JSTSP.2022.3160241 https://ieeexplore.ieee.org/document/9765320 https://dx.doi.org/10.1109/JSTSP.2022.3171168 http://www.signalprocessingsociety.org