CRNN-Refined Spatiotemporal Transformer for Dynamic MRI reconstruction.

Magnetic Resonance Imaging (MRI) plays a pivotal role in modern clinical practice, providing detailed anatomical visualization with exceptional spatial resolution and soft tissue contrast. Dynamic MRI, aiming to capture both spatial and temporal characteristics, faces challenges related to prolonged acquisition times and susceptibility to motion artifacts. Balancing spatial and temporal resolutions becomes crucial in real-world clinical scenarios.

Unsupervised spectral reconstruction from RGB images under two lighting conditions.

Unsupervised spectral reconstruction (SR) aims to recover the hyperspectral image (HSI) from corresponding RGB images without annotations. Existing SR methods achieve it from a single RGB image, hindered by the significant spectral distortion. Although several deep learning-based methods increase the SR accuracy by adding RGB images, their networks are always designed for other image recovery tasks, leaving huge room for improvement.

DCT-net: Dual-domain cross-fusion transformer network for MRI reconstruction.

Current challenges in Magnetic Resonance Imaging (MRI) include long acquisition times and motion artifacts. To address these issues, under-sampled k-space acquisition has gained popularity as a fast imaging method. However, recovering fine details from under-sampled data remains challenging.

Hyperspectral image super-resolution via spectral matching and correction.

Fusing a low-spatial-resolution hyperspectral image (LR-HSI) and a high-spatial-resolution RGB image (HR-RGB) is an important technique for HR-HSI obtainment. In this paper, we propose a dual-illuminance fusion-based super-resolution method consisting of spectral matching and correction. In the spectral matching stage, an LR-HSI patch is first searched for each HR-RGB pixel; with the minimum color difference as a constraint, the matching spectrum is constructed by linear mixing the spectrum in the HSI patch.

Hyperspectral image super-resolution via a multi-stage scheme without employing spatial degradation.

Recently, it has become popular to obtain a high spatial resolution hyperspectral image (HR-HSI) by fusing a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution RGB image (HR-RGB). Existing HSI super-resolution methods are designed based on a known spatial degeneration. In practice, it is difficult to obtain correct spatial degradation, which restricts the performance of existing methods.

Hyperspectral image super-resolution based on the transfer of both spectra and multi-level features.

Existing hyperspectral image (HSI) super-resolution methods fusing a high-resolution RGB image (HR-RGB) and a low-resolution HSI (LR-HSI) always rely on spatial degradation and handcrafted priors, which hinders their practicality. To address these problems, we propose a novel, to the best of our knowledge, method with two transfer models: a window-based linear mixing (W-LM) model and a feature transfer model. Specifically, W-LM initializes a high-resolution HSI (HR-HSI) by transferring the spectra from the LR-HSI to the HR-RGB.

Optimizing selection of the test color sample set for the CIE 2017 color fidelity index.

We have developed a new method for selecting the test color sample set (TCSS) used to calculate CIE 2017 color fidelity index (CIE-R). Taking a Large Set as a starting point, a new optimized color sample set (OCSS) is obtained by clustering analysis. Taking metamerism phenomenon into account, spectra clustering is performed within the class obtained from color appearance attributes clustering.

[A novel spatial modulation Fourier transform spectrometer with adjustable spectral resolution].

In the premise of fulfilling the application requirement, the adjustment of spectral resolution can improve efficiency of data acquisition, data processing and data saving. So, by adjusting the spectral resolution, the performance of spectrometer can be improved, and its application range can be extended. To avoid the problems of the fixed spectral resolution of classical Fourier transform spectrometer, a novel type of spatial modulation Fourier transform spectrometer with adjustable spectral resolution is proposed in this paper.