View-Aligned Nonlocal Low-Rank Tensor Reconstruction for Snapshot Compressive Multi-View Spectral Imaging System

Trans2-CBCT: A Dual-Transformer Framework for Sparse-View CBCT Reconstruction

Cone-beam computed tomography (CBCT) with sparse projection views offers reduced radiation dose and faster scans but introduces severe streak artifacts and spatial coverage gaps. We address these challenges within a unified framework. First, we replace conventional UNet/ResNet encoders with TransUNet, a hybrid CNN–Transformer architecture that jointly models local details and long-range spatial context. It is adapted to CBCT reconstruction by concatenating multi-scale feature maps and introducing a lightweight attenuation-prediction head. Trans-CBCT outperforms the best baseline by 1.17 dB in PSNR and by 0.0163 in SSIM on LUNA16 with only six projection views. Second, we incorporate a neighbor-aware Point Transformer with explicit 3D positional encodings and a neighbor-aware attention module aggregating information from each point’s k-nearest spatial neighbors to enforce volumetric coherence. The resulting Trans2-CBCT achieves an additional 0.63 dB increase in PSNR and 0.0117 increase in SSIM over Trans-CBCT. In experiments with 6-10 views, Trans-CBCT and Trans2-CBCT consistently outperform all prior methods in both PSNR and SSIM on LUNA16. On the ToothFairy dataset, Trans2-CBCT leads in five of the six measurements, outperforming all baselines in PSNR. These results highlight the effectiveness of combining hybrid CNN–Transformer features with geometry-aware point-based reasoning for sparse-view CBCT reconstruction.

GS$^{2}$CI: Robust Gaussian Splatting For Snapshot Compressive Imaging via Large Vision Model Priors

Snapshot Compressive Imaging (SCI) offers an efficient solution for high-speed video acquisition and, under exposure-time camera--scene relative motion, multi-view scene capture by compressing temporal or spatial information into a single 2D measurement. While recent studies have explored SCI for 3D scene reconstruction, existing methods struggle with significant challenges due to information loss, limited viewpoint diversity, and the computational burden of jointly optimizing 3D representations and camera poses. In this work, we propose a novel framework that reconstructs high-quality 3D scenes from a single SCI measurement by leveraging 3D Gaussian Splatting (3DGS) and the powerful priors of large-scale vision foundation models (VFMs). Our primary reconstruction combines measurement-derived 3D VFM initialization with SCI-aware Gaussian optimization. After coarse-stage convergence, an auxiliary 2D VFM provides pseudo-view supervision at synthesized viewpoints for local appearance refinement. To further address the instability caused by ambiguous SCI supervision during 3DGS optimization, we introduce Opacity-Guided Splitting and Growth Regulation (OSGR), an SCI-specific densification strategy that augments split candidates using local opacity statistics, discourages loss-compensating opacity inflation through mean-opacity regulation, and bounds representation growth with explicit candidate-ratio and Gaussian-count constraints. Extensive experiments across multiple benchmarks demonstrate that our method achieves the strongest overall performance, combining leading reconstruction quality and robustness to viewpoint variation with competitive computational efficiency.

CAFDIM: a group convolution and self-attention fusion-based dual-domain iterative method for sparse-view CT reconstruction

Objective. Sparse-view computed tomography (CT) reduces radiation dose and acquisition time by decreasing the number of projection views, but it also makes image reconstruction severely ill-posed, leading to structural distortion and severe artifacts. This study aims to develop an effective reconstruction framework for improving both projection-data fidelity and reconstructed image quality in sparse-view CT. Approach. We propose a group convolution- and self-attention fusion-based dual-domain iterative method (CAFDIM) for sparse-view CT reconstruction. CAFDIM follows a model-informed dual-domain iterative design. The framework consists of the initialization enhancement network, gradient update block, projection-domain repair network, image-domain repair network, and momentum update block. The projection-domain branch employs a deep sparse block to enhance sparse projection features before full-view projection restoration, while the image-domain branch uses edge-guided residual refinement to improve anatomical structure preservation. To enhance local-global feature representation, a Convolution-Attention Fusion Block is embedded into both repair branches by combining group convolution with Pixel Shift Self-Attention. Results. Experiments on simulated and real clinical projection datasets demonstrate that CAFDIM effectively suppresses sparse-view artifacts, preserves anatomical structures, and achieves superior reconstruction accuracy, visual quality, and generalization ability compared with state-of-the-art methods. Significance. CAFDIM provides an effective and efficient dual-domain reconstruction framework for sparse-view CT, showing strong potential for clinical applications in sparse-view and low-dose CT imaging.

Quaternion Tensor Modeling for Joint Color-Polarization Demosaicking

Division-of-focal-plane (DoFP) color polarization cameras enable snapshot acquisition of color polarization mosaic images, but the inherently sparse sampling pattern makes color polarization demosaicking severely ill-posed. Existing methods often fail to jointly exploit the correlations among polarization channels and the physical constraints inherent in polarization imaging, resulting in noticeable demosaicking artifacts. To address this issue, a quaternion-tensor-based color polarization demosaicking (CPDM) method incorporating Stokes-domain total variation (TV) regularization is proposed. Correlation analysis shows that the correlations among polarization channels are stronger than those among color channels. Accordingly, the color polarization images acquired at $0^\circ$, $45^\circ$, $90^\circ$, and $135^\circ$ are encoded into the four components of a third-order quaternion tensor, with the color channels organized along its third mode. A low-rank prior is then imposed on the quaternion tensor to exploit the global structural redundancy in the color polarization data. Moreover, spatial gradients are mapped to the Stokes domain through an orthogonal transformation to separate intensity, polarization and residual variations, with adaptive quaternion weights enabling component-specific regularization and preserving the energy consistency of the reconstructed Stokes vectors. An efficient optimization algorithm is derived for the resulting model. Extensive experiments demonstrate the superior demosaicking performance of the proposed method.

Multi-View Alignment and Denoising via Center-Guided Spectral Diffusion

Robust Tensor Recovery Using Second-Order Difference-Induced Adaptive Nuclear Norm

A robust tensor recovery model based on second-order difference-induced adaptive tensor nuclear norm regularization that consistently improves PSNR and ERGAS under all tested noise settings while achieving competitive SSIM values is proposed.