Spectral Compressive Imaging

Spectral compressive imaging (SCI) aims to reconstruct high-dimensional hyperspectral images from significantly lower-dimensional measurements, thereby reducing data acquisition time and storage needs. Current research heavily emphasizes deep learning approaches, particularly deep unfolding networks incorporating convolutional neural networks (CNNs) and transformers, to improve reconstruction accuracy and efficiency. These methods often focus on addressing challenges like ill-posed inverse problems, computational cost, and the need for robust handling of noise and various degradation patterns inherent in the measurement process. SCI holds significant promise for applications requiring rapid and efficient hyperspectral data acquisition, such as remote sensing, medical imaging, and industrial inspection.