A Deep Unfolding Framework for Diffractive Snapshot Spectral Imaging

• URL: http://arxiv.org/abs/2507.04622v1
• Date: Mon, 07 Jul 2025 02:49:49 GMT
• Title: A Deep Unfolding Framework for Diffractive Snapshot Spectral Imaging
• Abstract summary: We propose an efficient deep unfolding framework for diffractive systems, termed diffractive deep unfolding (DDU)<n>We derive an analytical solution for the data fidelity term in DSSI, ensuring both the efficiency and the effectiveness during the iterative reconstruction process.<n>Our framework demonstrates strong compatibility with existing state-of-the-art (SOTA) models.
• Score: 23.83874042309026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Snapshot hyperspectral imaging systems acquire spectral data cubes through compressed sensing. Recently, diffractive snapshot spectral imaging (DSSI) methods have attracted significant attention. While various optical designs and improvements continue to emerge, research on reconstruction algorithms remains limited. Although numerous networks and deep unfolding methods have been applied on similar tasks, they are not fully compatible with DSSI systems because of their distinct optical encoding mechanism. In this paper, we propose an efficient deep unfolding framework for diffractive systems, termed diffractive deep unfolding (DDU). Specifically, we derive an analytical solution for the data fidelity term in DSSI, ensuring both the efficiency and the effectiveness during the iterative reconstruction process. Given the severely ill-posed nature of the problem, we employ a network-based initialization strategy rather than non-learning-based methods or linear layers, leading to enhanced stability and performance. Our framework demonstrates strong compatibility with existing state-of-the-art (SOTA) models, which effectively address the initialization and prior subproblem. Extensive experiments validate the superiority of the proposed DDU framework, showcasing improved performance while maintaining comparable parameter counts and computational complexity. These results suggest that DDU provides a solid foundation for future unfolding-based methods in DSSI.

Related papers

• Efficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution [69.17533212524447]
  We propose a Difficulty-aware Dynamic Routing ( DDR) strategy that overcomes the rigid, one-size-fits-all processing paradigm.<n>Specifically, we design a difficulty estimator to predict the restoration cost of each input image, enabling automatic assignment to a network of appropriate capacity.<n>Extensive experiments have demonstrated the superior efficiency and effectiveness of the proposed model compared to recent state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-07-17T07:49:05Z)
• SDF-Net: Structure-Aware Disentangled Feature Learning for Opticall-SAR Ship Re-identification [10.512241383910721]
  Cross-modal ship re-identification (ReID) between optical and synthetic aperture radar (SAR) imagery is fundamentally challenged.<n>We propose SDF-Net, a Structure-Aware Disentangled Feature Learning Network that systematically incorporates geometric consistency into optical--SAR ship ReID.<n> experiments on the HOSS-ReID dataset demonstrate that SDF-Net consistently outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-03-13T02:41:43Z)
• DSA-SRGS: Super-Resolution Gaussian Splatting for Dynamic Sparse-View DSA Reconstruction [67.42242016220122]
  Digital subtraction angiography is a key imaging technique for the auxiliary diagnosis and treatment of cerebrovascular diseases.<n>Recent advancements in gaussian splatting and dynamic neural representations have enabled robust 3D vessel reconstruction from sparse dynamic inputs.<n>This paper proposes DSA-SRGS, the first super-resolution gaussian splatting framework for dynamic sparse-view DSA reconstruction.
  arXiv  Detail & Related papers  (2026-03-05T03:41:08Z)
• Hierarchical Spatial-Frequency Aggregation for Spectral Deconvolution Imaging [19.898033482653464]
  In this paper, we introduce a new CSI method to achieve high-fidelity compact matrix design.<n>To tackle the inherent data-dependent operators in SDI, we introduce a Hierarchical Spatial-Spectral Aggregation Unfolding Framework (HSFAUF)<n> Furthermore, to integrate spatial-spectral priors during iterative refinement, we propose a Spatial-Frequency Aggregation Transformer (SFAT)
  arXiv  Detail & Related papers  (2025-11-10T06:29:34Z)
• Total Variation Subgradient Guided Image Fusion for Dual-Camera CASSI System [3.414706880536149]
  Spectral imaging technology has long-faced fundamental challenges in balancing spectral, spatial, and temporal resolutions.<n>Traditional model-based methods exhibit limited performance due to reliance on handcrafted inherent image priors.<n>We propose a dual-camera CASSI reconstruction framework that integrates total variation (TV) subgradient theory.
  arXiv  Detail & Related papers  (2025-09-13T16:57:06Z)
• One-Step Diffusion-based Real-World Image Super-Resolution with Visual Perception Distillation [53.24542646616045]
  We propose VPD-SR, a novel visual perception diffusion distillation framework specifically designed for image super-resolution (SR) generation.<n>VPD-SR consists of two components: Explicit Semantic-aware Supervision (ESS) and High-frequency Perception (HFP) loss.<n>The proposed VPD-SR achieves superior performance compared to both previous state-of-the-art methods and the teacher model with just one-step sampling.
  arXiv  Detail & Related papers  (2025-06-03T08:28:13Z)
• Quaternion Wavelet-Conditioned Diffusion Models for Image Super-Resolution [7.986370916847687]
  We introduce ResQu, a novel SR framework that integrates a quaternion wavelet preprocessing framework with latent diffusion models.<n>Our approach enhances the conditioning process by exploiting quaternion wavelet embeddings, which are dynamically integrated at different stages of denoising.<n>Our method achieves outstanding SR results, outperforming in many cases existing approaches in perceptual quality and standard evaluation metrics.
  arXiv  Detail & Related papers  (2025-05-01T06:17:33Z)
• One-Step Diffusion Model for Image Motion-Deblurring [85.76149042561507]
  We propose a one-step diffusion model for deblurring (OSDD), a novel framework that reduces the denoising process to a single step.<n>To tackle fidelity loss in diffusion models, we introduce an enhanced variational autoencoder (eVAE), which improves structural restoration.<n>Our method achieves strong performance on both full and no-reference metrics.
  arXiv  Detail & Related papers  (2025-03-09T09:39:57Z)
• Coarse-Fine Spectral-Aware Deformable Convolution For Hyperspectral Image Reconstruction [15.537910100051866]
  We study the inverse problem of Coded Aperture Snapshot Spectral Imaging (CASSI) We propose Coarse-Fine Spectral-Aware Deformable Convolution Network (CFSDCN) Our CFSDCN significantly outperforms previous state-of-the-art (SOTA) methods on both simulated and real HSI datasets.
  arXiv  Detail & Related papers  (2024-06-18T15:15:12Z)
• Binarized Diffusion Model for Image Super-Resolution [61.963833405167875]
  Binarization, an ultra-compression algorithm, offers the potential for effectively accelerating advanced diffusion models (DMs) Existing binarization methods result in significant performance degradation. We introduce a novel binarized diffusion model, BI-DiffSR, for image SR.
  arXiv  Detail & Related papers  (2024-06-09T10:30:25Z)
• Untrained Neural Nets for Snapshot Compressive Imaging: Theory and Algorithms [15.54330224419903]
  Snapshot compressive imaging (SCI) recovers high-dimensional (3D) data cubes from a single 2D measurement. In this paper, we focus on SCI recovery algorithms that employ untrained neural networks (UNNs), such as deep image prior (DIP), to model source structure.
  arXiv  Detail & Related papers  (2024-06-06T02:22:43Z)
• DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral Diffusion Model [18.25548360119976]
  This paper endeavors to advance the precision of snapshot compressive imaging (SCI) reconstruction for multispectral image (MSI) We propose a novel structured zero-shot diffusion model, dubbed DiffSCI. We present extensive testing to show that DiffSCI exhibits discernible performance enhancements over prevailing self-supervised and zero-shot approaches.
  arXiv  Detail & Related papers  (2023-11-19T20:27:14Z)
• ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution [76.7408734079706]
  Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. We propose ESSAformer, an ESSA attention-embedded Transformer network for single-HSI-SR with an iterative refining structure.
  arXiv  Detail & Related papers  (2023-07-26T07:45:14Z)
• RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution [57.98314517861539]
  Burst super-resolution (BurstSR) aims at reconstructing a high-resolution (HR) image from a sequence of low-resolution (LR) and noisy images. In this paper, we suggest fusing cues frame-by-frame with an efficient and flexible recurrent network.
  arXiv  Detail & Related papers  (2023-06-30T12:14:13Z)
• End to end hyperspectral imaging system with coded compression imaging process [13.00211539170695]
  We present a physics-informed self-supervising CNN method based on a coded aperture spectral imaging system. Our method effectively exploits the spatial-spectral relativization from the coded spectral information and forms a self-supervising system based on the camera quantum effect model.
  arXiv  Detail & Related papers  (2021-09-06T13:39:54Z)
• A New Backbone for Hyperspectral Image Reconstruction [90.48427561874402]
  3D hyperspectral image (HSI) reconstruction refers to inverse process of snapshot compressive imaging. Proposal is for a Spatial/Spectral Invariant Residual U-Net, namely SSI-ResU-Net. We show that SSI-ResU-Net achieves competing performance with over 77.3% reduction in terms of floating-point operations.
  arXiv  Detail & Related papers  (2021-08-17T16:20:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.