19 research outputs found
Reconstruction of compressed spectral imaging based on global structure and spectral correlation
In this paper, a convolution sparse coding method based on global structure
characteristics and spectral correlation is proposed for the reconstruction of
compressive spectral images. The proposed method uses the convolution kernel to
operate the global image, which can better preserve image structure information
in the spatial dimension. To take full exploration of the constraints between
spectra, the coefficients corresponding to the convolution kernel are
constrained by the norm to improve spectral accuracy. And, to solve the problem
that convolutional sparse coding is insensitive to low frequency, the global
total-variation (TV) constraint is added to estimate the low-frequency
components. It not only ensures the effective estimation of the low-frequency
but also transforms the convolutional sparse coding into a de-noising process,
which makes the reconstructing process simpler. Simulations show that compared
with the current mainstream optimization methods (DeSCI and Gap-TV), the
proposed method improves the reconstruction quality by up to 7 dB in PSNR and
10% in SSIM, and has a great improvement in the details of the reconstructed
image
Computational Hyperspectral Imaging with Diffractive Optics and Deep Residual Network
Hyperspectral imaging critically serves for various fields such as remote sensing, biomedical and agriculture. Its potential can be exploited to a greater extent when combined with deep learning methods, which improve the reconstructed hyperspectral image quality and reduce the processing time. In this paper, we propose a novel snapshot hyperspectral imaging system using optimized diffractive optical element and color filter along with the residual dense network. We evaluate our method through simulations considering the effects of each optical element and noise. Simulation results demonstrate high-quality hyperspectral image reconstruction capabilities through the proposed computational hyperspectral camera.acceptedVersionPeer reviewe
MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multi-scale Dilated Convolution for Image Compressive Sensing (CS)
Compressive sensing (CS) is a technique that enables the recovery of sparse
signals using fewer measurements than traditional sampling methods. To address
the computational challenges of CS reconstruction, our objective is to develop
an interpretable and concise neural network model for reconstructing natural
images using CS. We achieve this by mapping one step of the iterative shrinkage
thresholding algorithm (ISTA) to a deep network block, representing one
iteration of ISTA. To enhance learning ability and incorporate structural
diversity, we integrate aggregated residual transformations (ResNeXt) and
squeeze-and-excitation (SE) mechanisms into the ISTA block. This block serves
as a deep equilibrium layer, connected to a semi-tensor product network
(STP-Net) for convenient sampling and providing an initial reconstruction. The
resulting model, called MsDC-DEQ-Net, exhibits competitive performance compared
to state-of-the-art network-based methods. It significantly reduces storage
requirements compared to deep unrolling methods, using only one iteration block
instead of multiple iterations. Unlike deep unrolling models, MsDC-DEQ-Net can
be iteratively used, gradually improving reconstruction accuracy while
considering computation trade-offs. Additionally, the model benefits from
multi-scale dilated convolutions, further enhancing performance.Comment: 15 pages, 8 figures, open access journal pape