Spectral image reconstruction using an edge preserving spatio-spectral Wiener estimation

Urban, Philipp; Rosen, Mitchell R.; Berns, Roy S.

doi:10.1364/josaa.26.001865

Cited by 24 publications

(17 citation statements)

References 19 publications

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“…Therefore, a priori knowledge on spectral reflectances is used to obtain reasonable results: Maloney et al used a low-dimensional linear reflectance model [13], other researchers picked the smoothest reflectance [12,3,14], principal component analysis and Wiener estimation [21], adaptive principal component analysis [25], extend the linear reflectance model to manifolds [4] or use kernel-based methods [6]. For capturing images, methods were proposed that include a priori knowledge on inter-pixel correlation [15,22] and the point spread function of the acquisition system [8].…”

Section: Related Workmentioning

confidence: 99%

Trichromatic Reflectance Capture Using a Tunable Light Source: Setup, Characterization and Reflectance Estimation

Tanksale¹,

Urban²

2016

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A research project is underway to develop a gonio imager particularly dedicated to sample the Bidirectional Reflectance Distribution Function (BRDF) of materials and material compositions employed and created by multimaterial 3D printers. It comprises an almost colorimetric RGB camera and a spectrally tunable light source. In this paper, we investigate an important part of this system, particularly the approach to estimate reflectances from RGB values acquired under multiple illuminants. We first characterize the system by estimating the spectral sensitivities of the camera. Then, we use the sensitivities, a set of illuminants produced by the tunable light source and the corresponding sensor responses to estimate reflectances. For evaluating this approach, we measure the Neugebauer primary reflectances of a polyjet printer employing highly translucent photo-polymer printing materials colored in cyan, magenta, yellow and white. Spectral and colorimetric deviations to spectroradiometric comparison measurements (average 0.67 CIEDE2000 units / 0.0286 spectral RMS) are within the inter-instrument variability of hand-held spectrophotometers used in graphic arts for prints on paper.

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Section: Related Workmentioning

confidence: 99%

Trichromatic Reflectance Capture Using a Tunable Light Source: Setup, Characterization and Reflectance Estimation

Tanksale¹,

Urban²

2016

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“…(20). In the following sections, two criteria are developed to control the system to create an efficient inverse model.…”

Section: Spectral Signal Recoverymentioning

confidence: 99%

“…(20), it can be seen that any deviation of c results in a concomitant deviation in the approximated spectral signal, r. Therefore, it is preferable to have a model that is robust to the noise of the system so that the recovered signalr deviates as little as possible while c is perturbed by the inevitable noise of the recording device. The derivative of the recovered signalr of Eq.…”

Section: B Perturbation Of the Systemmentioning

confidence: 99%

“…In the Bayesian inference model for inverse problems the posterior distribution is obtained by prior knowledge of the signal and noise. The Wiener filter restoration method, resulting from Bayes theorem, is a common technique for signal restoration, image reconstruction and communications problems [3], [17]- [20]. In 1995 Brainard suggested that Bayesian method might be suitable for spectral recovery from the RGB sensor responses [21].…”

Section: Introductionmentioning

confidence: 99%

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Generalized Inverse-Approach Model for Spectral-Signal Recovery

Peyvandi¹,

Amirshahi

Hernández‐Andrés

et al. 2013

IEEE Trans. on Image Process.

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We have studied the transformation system of a spectral signal to the response of the system as a linear mapping from higher to lower dimensional space in order to look more closely at inverse-approach models. The problem of spectral-signal recovery from the response of a transformation system is generally stated on the basis of the generalized inverse-approach theorem, which provides a modular model for generating a spectral signal from a given response value. The controlling criteria, including the robustness of the inverse model to perturbations of the response caused by noise, and the condition number for matrix inversion, are proposed, together with the mean square error, so as to create an efficient model for spectral-signal recovery. The spectral-reflectance recovery and color correction of natural surface color are numerically investigated to appraise different illuminant-observer transformation matrices based on the proposed controlling criteria both in the absence and the presence of noise.

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“…In recent years, many methods have been introduced, among which Wiener estimation [2][3][4] and pseudoinverse [1,5] were widely deployed in the literature. We note that, in many previous studies, the calibration samples and test samples are of identical medium such as the standard ColorChecker charts [6].…”

Section: Introductionmentioning

confidence: 99%

Correcting cross-media instrument metamerism for reflectance estimation in multispectral imaging

et al. 2011

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In multispectral imaging, the color accuracy of spectral reflectance estimation degrades significantly if the medium of test samples is different from that of calibration samples. This occurs mainly for two reasons, i.e., the different characteristics of spectral reflectances and the different measurement principles between an imaging system and a spectrophotometer. In this paper, this problem is referred to as cross-media instrument metamerism. We propose to correct it by using calibration samples from a standard color chart and a limited number of tuning samples with a target medium as a priori knowledge. The reflectance transform is computed by using both calibration and tuning samples, and the metamerism transform is calculated by modeling the correlation of camera responses between neighboring imaging channels. Experimental results show that the proposed method produces satisfactory spectral and colorimetric accuracy in reflectance estimation. The method could be deployed in practical applications when the available samples of certain media are inadequate for accurate reflectance estimation in a multispectral imaging system.

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Spectral image reconstruction using an edge preserving spatio-spectral Wiener estimation

Cited by 24 publications

References 19 publications

Trichromatic Reflectance Capture Using a Tunable Light Source: Setup, Characterization and Reflectance Estimation

Trichromatic Reflectance Capture Using a Tunable Light Source: Setup, Characterization and Reflectance Estimation

Generalized Inverse-Approach Model for Spectral-Signal Recovery

Correcting cross-media instrument metamerism for reflectance estimation in multispectral imaging

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