Structure-preserving spectral reflectance estimation using guided filtering

Sippel, Frank; Seiler, Jurgen; Genser, Nils; Kaup, André

doi:10.1364/josaa.400485

Cited by 5 publications

(8 citation statements)

References 31 publications

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“…where the maximum value of the hyperspectral images is one, and W and H is the image width and height, respectively. Our novel method CSR is compared to the SSW [7] and the state-of-the-art algorithm structure-preserving reflectance estimation (SPRE) [8] as well as to the application of the BM3D [9]. Specifically, the multispectral image are denoised using BM3D first and the reconstruction is done using a Wiener filter [5] afterwards (PreBM3D), and vice versa, namely, first the reconstruction with a Wiener filter and afterwards the application of the BM3D (PostBM3D).…”

Section: Discussionmentioning

confidence: 99%

“…The covariance matrix K s can be derived from minimizing the second order differences. The optimization problem for the noiseless case with second order difference matrix D 2 [8]…”

Section: Proposed Methodsmentioning

confidence: 99%

“…This is basically the same equation as in (8), where the covariance matrix K s replaces the smoothing matrix M −1 . Thus, the covariance matrix is set to…”

Section: Applying the Wiener Filter To A Single Pixel Without Noise R...mentioning

confidence: 99%

“…For example the spatiospectral Wiener filter (SSW) [7] tries to lower the influence of noise by modeling the neighborhood in vertical and horizontal direction as a first-order Markov process. Another approach is to use guided filtering to improve the reconstruction result, which is called structure-preserving reflectance estimation (SPRE) [8]. SPRE and SSW perform well for a medium amount of noise, however in environments with strong noise they also fail to deliver good results in terms of measurable error, as well as from a visual point of view.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Image Reconstruction from Multispectral Images Using Non-Local Filtering

Sippel

Seiler

Kaup

2021

2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP)

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Using light spectra is an essential element in many applications, for example, in material classification. Often this information is acquired by using a hyperspectral camera. Unfortunately, these cameras have some major disadvantages like not being able to record videos. Therefore, multispectral cameras with wide-band filters are used, which are much cheaper and are often able to capture videos. However, using multispectral cameras requires an additional reconstruction step to yield spectral information. Usually, this reconstruction step has to be done in the presence of imaging noise, which degrades the reconstructed spectra severely. Typically, same or similar pixels are found across the image with the advantage of having independent noise. In contrast to state-of-the-art spectral reconstruction methods which only exploit neighboring pixels by block-based processing, this paper introduces non-local filtering in spectral reconstruction. First, a block-matching procedure finds similar non-local multispectral blocks. Thereafter, the hyperspectral pixels are reconstructed by filtering the matched multispectral pixels collaboratively using a reconstruction Wiener filter. The proposed novel procedure even works under very strong noise. The method is able to lower the spectral angle up to 18% and increase the peak signal-to-noise-ratio up to 1.1dB in noisy scenarios compared to state-of-the-art methods. Moreover, the visual results are much more appealing.

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Section: Discussionmentioning

confidence: 99%

“…The covariance matrix K s can be derived from minimizing the second order differences. The optimization problem for the noiseless case with second order difference matrix D 2 [8]…”

Section: Proposed Methodsmentioning

confidence: 99%

“…This is basically the same equation as in (8), where the covariance matrix K s replaces the smoothing matrix M −1 . Thus, the covariance matrix is set to…”

Section: Applying the Wiener Filter To A Single Pixel Without Noise R...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hyperspectral Image Reconstruction from Multispectral Images Using Non-Local Filtering

Sippel

Seiler

Kaup

2021

2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP)

Self Cite

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“…To achieve this, an imaging pipeline needs to be simulated. A single pixel of the i-th channel of a multispectral image is recorded by [32]…”

Section: A3 Evaluationmentioning

confidence: 99%

Synthetic hyperspectral array video database with applications to cross-spectral reconstruction and hyperspectral video coding

Sippel¹,

Seiler²,

Kaup³

2023

J. Opt. Soc. Am. A

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In this paper, a synthetic hyperspectral video database is introduced. Since it is impossible to record ground truth hyperspectral videos, this database offers the possibility to leverage the evaluation of algorithms in diverse applications. For all scenes, depth maps are provided as well to yield the position of a pixel in all spatial dimensions as well as the reflectance in spectral dimension. Two novel algorithms for two different applications are proposed to prove the diversity of applications that can be addressed by this novel database. First, a cross-spectral image reconstruction algorithm is extended to exploit the temporal correlation between two consecutive frames. The evaluation using this hyperspectral database shows an increase in PSNR of up to 5.6 dB dependent on the scene. Second, a hyperspectral video coder is introduced which extends an existing hyperspectral image coder by exploiting temporal correlation. The evaluation shows rate savings of up to 10% depending on the scene. The novel hyperspectral video database and source code is available at https:// github.com/ FAU-LMS/ HyViD for use by the research community.

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LED Degradation Monitoring Using a Multi-Channel Spectral Sensor

Myland,

Herzog,

Babilon

et al. 2024

IEEE Access

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This study explores a novel approach to monitor the spectral emission of LEDs by estimating the spectral power distribution from the spectral sensor responses during an accelerated ageing experiment. Two methods for reconstructing the actual LED spectra from sensor responses are presented and tested, one solely requires sensor datasheet information and the other uses a full spectral characterisation of the sensor's spectral sensitivities. The reconstruction results show that a spectral sensor can provide accurate spectral estimates even after severe LED degradation. Only for an LED that suffered a phosphor crack, affecting its spatial radiation characteristics, limited ability to estimate the true spectral power distribution without prior assumptions about the spectral changes must be reported. Overall, the use of a spectral sensor, even without detailed characterisation of the sensor itself, allows for an accurate monitoring of the true emission of LEDs, with a maximum radiometric error of 0.73 %, a maximum colormetric error of 0.0017 ∆u ′ v ′ and a maximum spectral nRMSE error of 0.0097 compared to a spectroradiometric measurement. This advance holds great promise for improving lighting technology, particularly in applications that require constant radiometric output and stable color.

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Structure-preserving spectral reflectance estimation using guided filtering

Cited by 5 publications

References 31 publications

Hyperspectral Image Reconstruction from Multispectral Images Using Non-Local Filtering

Hyperspectral Image Reconstruction from Multispectral Images Using Non-Local Filtering

Synthetic hyperspectral array video database with applications to cross-spectral reconstruction and hyperspectral video coding

LED Degradation Monitoring Using a Multi-Channel Spectral Sensor

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