Hyperspectral image reconstruction by deep learning with super-Rayleigh speckles
Ghost imaging via sparsity constraints (GISC) spectral camera modulates the three-dimensional (3D) hyperspectral image into a two-dimensional (2D) compressive image with speckles in a single shot. It obtains a 3D hyperspectral image (HSI) by reconstruction algorithms. The rapid development of deep learning has provided a new method for 3D HSI reconstruction. Moreover, the imaging performance of the GISC spectral camera can be improved by optimizing the speckle modulation. In this paper, we propose an end-to-end GISCnet with super-Rayleigh speckle modulation to improve the imaging quality of the GISC spectral camera. The structure of GISCnet is very simple but effective, and we can easily adjust the network structure parameters to improve the image reconstruction quality. Relative to Rayleigh speckles, our super-Rayleigh speckles modulation exhibits a wealth of detail in reconstructing 3D HSIs. After evaluating 648 3D HSIs, it was found that the average peak signal-to-noise ratio increased from 27 dB to 31 dB. Overall, the proposed GISCnet with super-Rayleigh speckle modulation can effectively improve the imaging quality of the GISC spectral camera by taking advantage of both optimized super-Rayleigh modulation and deep-learning image reconstruction, inspiring joint optimization of light-field modulation and image reconstruction to improve ghost imaging performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningImage ReconstructionSimilar Papers 제목 키워드 기반
Voronoi-guided Bilateral 2D Gaussian Splatting for Arbitrary-Scale Hyperspectral Image Super-Resolution
Most existing hyperspectral image super-resolution methods require modifications for different scales, limiting their flexibility in arbitrary-scale reconstruction. 2D Gaussian splatting provides a continuous representat…
Spectral ReconstructionImage Super-ResolutionScalable low dimensional manifold model in the reconstruction of noisy and incomplete hyperspectral images
We present a scalable low dimensional manifold model for the reconstruction of noisy and incomplete hyperspectral images. The model is based on the observation that the spatial-spectral blocks of a hyperspectral image ty…
Unsupervised Spatial-spectral Hyperspectral Image Reconstruction and Clustering with Diffusion Geometry
Hyperspectral images, which store a hundred or more spectral bands of reflectance, have become an important data source in natural and social sciences. Hyperspectral images are often generated in large quantities at a re…
ClusteringImage ReconstructionAccurate reconstruction of EBSD datasets by a multimodal data approach using an evolutionary algorithm
A new method has been developed for the correction of the distortions and/or enhanced phase differentiation in Electron Backscatter Diffraction (EBSD) data. Using a multi-modal data approach, the method uses segmented im…
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction
Hyperspectral imaging is one of the most promising techniques for intraoperative tissue characterisation. Snapshot mosaic cameras, which can capture hyperspectral data in a single exposure, have the potential to make a r…
Decision MakingDemosaickingHyperspectral Image Super-ResolutionImage Generation+3