Hyperspectral image (HSI) joint super-resolution (SR) in both spatial and spectral dimensions is an area of increasing interest in HSI processing. Although recent advances in deep learning (DL) frameworks have greatly improved the performance of joint SR reconstruction, existing methods learn discrete representations of HSI, ignoring real-world signals' continuous nature. In this paper, we propose a joint SR method based on implicit neural representation (INR), which learns local continuous representations of high spatial resolution hyperspectral images from the discrete inputs. Experiments on joint SR demonstrate that our method can achieve superior performance in comparison with state-of-the-art methods.
In the field of compressive sensing spectral imaging, an adaptive coding method based on a-prior knowledge is a way to obtain high-precision scene information. In this paper, we propose a method that uses low-resolution spatial-spectral information to split into homogeneous regions before generating adaptive coding matrices, in response to the shortcomings of most existing adaptive coding methods that use only spatial a priori information to generate coding matrices. The method uses coding devices in a compressive spectral imaging system to obtain spectral a-priori information with low spatial resolution. Based on this a-priori information, an adaptive segmentation method with region merging is used to obtain segmented images with certain regional homogeneity. The adaptive coding theory and this segmentation result are combined to generate the adaptive coding matrix, and then the compressive observation information of the scene and its complementary observation information are obtained. Based on these observations, the scene information with a high spatial resolution is calculated by the reconstruction algorithm. Simulation experiments show that the adaptive compressive coding method based on spectral image region segmentation has advantages in peak signal-to-noise ratio and structural consistency rating indexes compared with traditional adaptive coding methods.
The liquid crystal modulator devices (LCMD) have become an important technique in the field of hyperspectral imaging. However, the spectral resolution and accuracy of LCMD-based imaging spectrometers are limited due to their principle. To break this limitation and promote the application of LCMD, we propose a spectral reconstruction method using model-based neural networks. The calibrated spectral transmittance of LCMD and a carefully designed loss function are used to constraint the calculation. Experiments on reconstructing both substance spectra and spectral image cubes have validated the effectiveness and superiority of the proposed method.
The conventional Fourier ptychographic microscopy (FPM) is a computational imaging approach, which stitches together a sequence of low-resolution (LR) images captured by different angles illumination. However, the limitation of processing efficiency in capturing LR images is gradually becoming obvious. Utilizing the principle, aimed at reducing the amount of captured measurements and decreasing acquisition time, this paper proposes an optimized spectral sampling scheme. In this method, the importance of the spectra in the spectrum domain is analyzed and the more informative parts are selected. The acquisition efficiency can be increased because the selected images are captured and applied into the conventional FPM routine. Compared with the conventional FPM, experimental results significantly indicate that the redundancy of information and the time of image collection could decrease without debasing the quality of the reconstruction.
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