ADMM-DSP: A Deep Spectral Image Prior for Snapshot Spectral Image Demosaicing

Chen, Yurong; Zhang, Hui; Wang, Yaonan; Ying, Ating; Zhao, Bingrui

doi:10.1109/tii.2023.3329674

Cited by 4 publications

(2 citation statements)

References 37 publications

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“…Let ∆ c s1 (i, j) represent the center pixel of the channel c requiring interpolation in the first step. The weight and weight-sum expressions in step 1 are given by ( 18) and (19), where s0 refers to step 0, s1 refers to step 1, and γ represents the weights.…”

Section: Directional Multispectral Demosaicingmentioning

confidence: 99%

“…This method is suitable for any non-redundant 4 × 4 MSFA. In addition, a deep-learning-based multispectral-demosaicing method has been developed [16][17][18][19], which typically produces better results than traditional methods. However, deep-learning-based multispectral-demosaicing methods have a smaller dataset compared to deep-learningbased Bayer-filter-demosaicing methods.…”

Section: Introductionmentioning

confidence: 99%

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Multispectral Demosaicing Based on Iterative-Linear-Regression Model for Estimating Pseudo-Panchromatic Image

Jeong,

Kim,

Kang

2024

Sensors

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This paper proposes a method for demosaicing raw images captured by multispectral cameras. The proposed method estimates a pseudo-panchromatic image (PPI) via an iterative-linear-regression model and utilizes the estimated PPI for multispectral demosaicing. The PPI is estimated through horizontal and vertical guided filtering, with the subsampled multispectral-filter-array-(MSFA) image and low-pass-filtered MSFA as the guide image and filtering input, respectively. The number of iterations is automatically determined according to a predetermined criterion. Spectral differences between the estimated PPI and MSFA are calculated for each channel, and each spectral difference is interpolated using directional interpolation. The weights are calculated from the estimated PPI, and each interpolated spectral difference is combined using the weighted sum. The experimental results indicate that the proposed method outperforms the State-of-the-Art methods with regard to spatial and spectral fidelity for both synthetic and real-world images.

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Section: Directional Multispectral Demosaicingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multispectral Demosaicing Based on Iterative-Linear-Regression Model for Estimating Pseudo-Panchromatic Image

Jeong,

Kim,

Kang

2024

Sensors

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Wavelength-Embedding-Guided Filter-Array Transformer for Spectral Demosaicing

Zeng,

Luong,

Philips

2024

Lecture Notes in Computer Science

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Analysis of the impact of temperature on the spectral shift in ultraviolet hyperspectral imaging spectrometers

Pan,

Li,

et al. 2024

Opt. Express

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The imaging spectrometer's high performance in practical applications may be compromised by environmental factors, particularly temperature variations, posing a challenge to its stability. Temperature fluctuations can induce spectral shift, directly impacting the accuracy of spectral measurements, subsequently influencing the precision of radiometric measurements. To address this issue, this study investigates a dual-channel UV imaging spectrometer. This instrument boasts a wavelength calibration accuracy of 0.01 nm. This paper conducts an in-depth analysis of the various mechanisms through which temperature changes influence the spectral line offset in the imaging spectrometer, integrating actual orbital temperature data to discuss the instrument's temperature load settings. The impact of temperature on spectral shift is examined using finite element analysis and optical design software. Estimations of spectral shift were made based on temperature variations. Simulation results indicated that the maximum deviation of spectral shift is estimated at 0.018 nm under a temperature condition of 16 ± 1°C. Under a more controlled orbital temperature condition (16 ± 0.3°C), the maximum deviation of spectral shift decreased to 0.01 nm. Experimental data revealed that at 16 ± 1°C, the maximum deviation of spectral shift did not exceed 0.01 nm. This effectively corroborates our theoretical analysis. The relationship between temperature and spectral shift offers a crucial theoretical foundation for calibrating spectral measurements and managing the thermal conditions of the instrument.

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ADMM-DSP: A Deep Spectral Image Prior for Snapshot Spectral Image Demosaicing

Cited by 4 publications

References 37 publications

Multispectral Demosaicing Based on Iterative-Linear-Regression Model for Estimating Pseudo-Panchromatic Image

Multispectral Demosaicing Based on Iterative-Linear-Regression Model for Estimating Pseudo-Panchromatic Image

Wavelength-Embedding-Guided Filter-Array Transformer for Spectral Demosaicing

Analysis of the impact of temperature on the spectral shift in ultraviolet hyperspectral imaging spectrometers

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