Oscillating Dispersion for Maximal Light-throughput Spectral Imaging
Authors: Jiuyun Zhang, Zhan Shi, Linsen Chen, Xun Cao
Organizations: Nanjing University, Nanjing, China · Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE, Nanjing University, Nanjing, China
Existing computational spectral imaging systems typically rely on coded aperture and beam splitters that block a substantial fraction of incident light, degrading reconstruction quality under light-starved conditions. To address this limitation, we develop the Oscillating Dispersion Imaging Spectrometer (ODIS), which for the first time achieves near-full light throughput by axially translating a disperser between the conjugate image plane and a defocused position, sequentially capturing a panchromatic (PAN) image and a dispersed measurement along a single optical path. We further propose a PAN-guided Dispersion-Aware Deep Unfolding Network (PDAUN) that recovers high-fidelity spectral information from maskless dispersion under PAN structural guidance. Its data-fidelity step derives an FFT-Woodbury preconditioned solver by exploiting the cyclic-convolution property of the ODIS forward model, while a Dispersion-Aware Deformable Convolution module (DADC) corrects sub-pixel spectral misalignment using PAN features. Experiments show state-of-the-art performance on standard benchmarks, and cross-system comparisons confirm that ODIS yields decisive gains under low illumination. High-fidelity reconstruction is validated on a physical prototype.
Figures & tables
Figure 1: Depiction of different computational spectral imaging systems. From left to right: coded-aperture systems modulate light with an amplitude mask; spectral-filter-based and diffraction-based systems encode spectral information via wavelength-selective filters or diffractive elements, respectively; ODIS encodes spectra through axial prism translation with a grayscale camera.
Figure 2: Optical layout of ODIS. (a) With the Amici prism at the image-conjugate plane, all wavelengths from a scene point converge to a common sensor location, producing a sharp PAN image. (b) Translating the prism along the optical axis to the defocused plane causes each wavelength to focus at a distinct lateral position, yielding a spectrally dispersed measurement.
Figure 3: Overview of PDAUN. (a) PDAUN architecture with K unfolding stages. (b) Denoiser with three-layer U-shaped structure. (c) FFT-Woodbury preconditioned conjugate-gradient solver. (d) Dispersion-Aware Deformable Convolution (DADC) module.
Figure 4: Cross-system simulation HSI reconstruction comparison under low illumination ( 9 lux). The bottom-left illustrates normalized spectrum curves at the position marked by pink triangle in the PAN image. The right shows reconstruction results at spectral bands 476.5nm, 536.5nm, 584.3nm, 648.1nm. Zoom in for a better view.
Method
Type
Params (M)
GFLOPs
PSNR ↑
SSIM ↑
SAM ↓
SIFDB [ 42 ]
Iterative
–
–
28.04
0.7714
19.45
TSA-Net [ 28 ]
E2E
44.252
135.21
35.88
0.9661
7.89
HDNet [ 17 ]
E2E
2.368
154.79
34.62
0.9593
8.86
MST [ 4 ]
Transformer
3.665
16.74
36.41
0.9697
8.49
CST [ 3 ]
Transformer
1.360
16.16
35.23
0.9631
9.04
SPECAT [ 39 ]
Transformer
0.286
11.45
35.17
0.9592
10.27
Table 3: Quantitative comparison of reconstruction algorithms on the ODIS simulation benchmark. All deep unfolding methods are configured with 5 stages. Best results are in bold and second best are underlined .
Figure 5: Real-world reconstruction on the ODIS prototype. (a) Raw measurements under standard illumination ( ≈ 774 lux); spectral curves at the position marked by a red sun are compared with spectrometer references. (b) Reconstructed 21-channel HSI and (c) its pseudo-RGB rendering. (d), (e) Corresponding results under low-light conditions ( ≈ 6.7 lux). (f) Photograph of the ODIS prototype.
Baseline
DADC
FFT-WB PCG
PSNR ↑
SSIM ↑
SAM ↓
✓
–
–
36.32
0.9684
8.77
✓
–
✓
36.47
0.9681
8.85
✓
✓
–
36.82
0.9702
8.07
✓
✓
✓
37.01
0.9736
8.09
Table 4: Ablation studies. (a) Break-down ablation on PDAUN’s two core components: DADC (dispersion-aware deformable convolution) and FFT-WB PCG (FFT-Woodbury preconditioned CG). (b) Effect of PCG iteration count.
Snapshot Spectral Imaging (SSI) provides high-dimensional temporal-spatial-spectral observation to uncover intrinsic physical characteristics. However, its complex system and repetitive calibration requirements hinder edge applications. Here, we propose a compact, cost-effective, calibration-free SSI method, Aperture Diffraction Imaging Spectrometer (ADIS), which consists only of a diffractive lens with a binary mask and a Bayer-filtered sensor, requiring no additional physical footprint compared to standard RGB cameras. ADIS disperses and multiplexes wavelengths, mapping energy to distinct sensor locations, enabling full-resolution recovery from superpixel-level encodings. ADIS directly leverages theoretically computed PSFs to enable calibration-free spectral reconstruction, while tolerating lens-dependent variations across different optical configurations and bridging the gap between simulation and reality. To achieve SSI by solving a sparsely-constrained inverse problem, we introduce the Orthogonal Diffraction-Aware Unfolding Framework (ODAUF) with Voxel Shift Transformer (VST) for improved orthogonal diffraction perception. Integrating VST into ODAUF forms the efficient Orthogonal Diffraction-Aware Unfolding Voxel Shift Transformer (ODAUVST), delivering excellent recovery and reduced parameters. By elaborating on theory, systematic and comprehensive comparing, and demonstrating real SSI results, we validate the superiority of ADIS, achieving calibration-free full-resolution SSI within a commercial camera footprint.
Enhancing perceptual dimensions while miniaturizing imaging systems presents significant challenges for high-dimensional visual sensing. Conventionally, the acquisition of the 5D (x,y,u,v,λ) spectral light field (5D-SLF) data cube relies on bulky and expensive camera arrays, which are impractical for widespread application. Existing single-detector systems are fundamentally limited by a trade-off between the resolutions of different dimensions owing to insufficient coding capabilities. Here we introduce an Aperture-aware Dispersion Light-field Imaging Spectrometer (ADLIS), that targets a synergy between compactness and resolution through aperture-multiplexed modulation, leveraging the inherent spectral-filtering properties of birefringent material. Using only a manufacturing-friendly and cost-effective phase plate made of birefringent quartz crystal, the aperture of the proposed ADLIS enables compact angular-spectral encoding that is highly sensitive to both the incident angle and spectrum of incoming light. In contrast to the viewpoint-separation approach of microlens arrays, ADLIS employs aperture encoding to superimpose all viewpoints onto each sensor pixel. This shifts the design paradigm from spatial division to encoding integration, aiming to achieve full-resolution light field recovery. Thus, we develop the Aperture-aware Dispersion Light-field Imaging (ADLI) framework, which optimizes the aperture design and 5D-SLF reconstruction in an end-to-end (E2E) manner. Trained by simulation data and validated through real-world experiments, our system achieves robust high-performance 5D-SLF imaging while maintaining full spatial resolution.
Chenglong Huang, Tao Lv, Jianing Yang +3
Nanjing University, Nanjing, 210023, China · Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE, Nanjing University
Snapshot hyperspectral imaging avoids sequential scanning, but systems that jointly achieve stable reconstruction, low cost, and compact optics remain limited. We present a snapshot hyperspectral imaging system based on angular-to-spectral diversity conversion. A tapered kaleidoscope creates replicated views with distinct incidence directions, and a directly attached birefringent filter converts them into view-channel-dependent spectral transmittances, yielding complementary measurements that better condition the inverse problem for more stable single-shot spectral reconstruction. The system preserves a simple pixel-wise linear model for fast non-learning-based reconstruction and uses only off-the-shelf components without relay optics or cascaded modules. We select the birefringent filter configuration using a condition-number-based criterion and validate the system on both synthetic and real data.