Hyperspectral image denoising: From model-driven, data-driven, to model-data-driven
Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications.
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …
Many-to-many splatting for efficient video frame interpolation
Motion-based video frame interpolation commonly relies on optical flow to warp pixels from
the inputs to the desired interpolation instant. Yet due to the inherent challenges of motion …
the inputs to the desired interpolation instant. Yet due to the inherent challenges of motion …
Herosnet: Hyperspectral explicable reconstruction and optimal sampling deep network for snapshot compressive imaging
Hyperspectral imaging is an essential imaging modality for a wide range of applications,
especially in remote sensing, agriculture, and medicine. Inspired by existing hyperspectral …
especially in remote sensing, agriculture, and medicine. Inspired by existing hyperspectral …
Hyperspectral compressive snapshot reconstruction via coupled low-rank subspace representation and self-supervised deep network
Coded aperture snapshot spectral imaging (CASSI) is an important technique for capturing
three-dimensional (3D) hyperspectral images (HSIs), and involves an inverse problem of …
three-dimensional (3D) hyperspectral images (HSIs), and involves an inverse problem of …
HLRTF: Hierarchical low-rank tensor factorization for inverse problems in multi-dimensional imaging
Inverse problems in multi-dimensional imaging, eg, completion, denoising, and compressive
sensing, are challenging owing to the big volume of the data and the inherent ill-posedness …
sensing, are challenging owing to the big volume of the data and the inherent ill-posedness …
Self-supervised nonlinear transform-based tensor nuclear norm for multi-dimensional image recovery
Recently, transform-based tensor nuclear norm (TNN) minimization methods have received
increasing attention for recovering third-order tensors in multi-dimensional imaging …
increasing attention for recovering third-order tensors in multi-dimensional imaging …
Meta‐Attention Network based Spectral Reconstruction with Snapshot Near‐Infrared Metasurface
H He, Y Zhang, Y Shao, Y Zhang, G Geng… - Advanced …, 2024 - Wiley Online Library
Near‐infrared (NIR) spectral information is important for detecting and analyzing material
compositions. However, snapshot NIR spectral imaging systems still pose significant …
compositions. However, snapshot NIR spectral imaging systems still pose significant …
Video frame interpolation with many-to-many splatting and spatial selective refinement
In this work, we first propose a fully differentiable Many-to-Many (M2M) splatting framework
to interpolate frames efficiently. Given a frame pair, we estimate multiple bidirectional flows …
to interpolate frames efficiently. Given a frame pair, we estimate multiple bidirectional flows …
HASIC-Net: Hybrid attentional convolutional neural network with structure information consistency for spectral super-resolution of RGB images
Spectral super-resolution (SSR), referring to the recovery of a reasonable hyperspectral
image (HSI) from a single RGB image, has achieved satisfactory performance as part of the …
image (HSI) from a single RGB image, has achieved satisfactory performance as part of the …
DUF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion
Compressive spectral imaging (CSI) has attracted significant attention since it employs
synthetic apertures to codify spatial and spectral information, sensing only 2D projections of …
synthetic apertures to codify spatial and spectral information, sensing only 2D projections of …