Sequential adaptive estimation for spectral reflectance based on camera responses

Wang, Lixia; Wan, Xiaoxia; Xiao, Gensheng; Liang, Jinxing

doi:10.1364/oe.389614

Cited by 18 publications

(20 citation statements)

References 23 publications

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“…In recent years, hyperspectral reconstruction from RGB images has become a very active research topic. A large number of methods have been proposed to reconstruct hyperspectral information using only RGB cameras [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. In general, these methods fall into three branches: traditional, machine learning, and deep learning methods.…”

Section: Introductionmentioning

confidence: 99%

“…In general, these methods fall into three branches: traditional, machine learning, and deep learning methods. Traditional methods include classical methods on the basis of Wiener estimation, pseudo-inverse estimation, or principal component analysis, and their various modifications, such as adaptive Wiener estimation [16], regularized local linear models [17], sequential weighted nonlinear regression models [18], and so on. Classical methods are simple and straight but not very accurate.…”

Section: Introductionmentioning

confidence: 99%

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Densely Residual Network with Dual Attention for Hyperspectral Reconstruction from RGB Images

2022

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In the last several years, deep learning has been introduced to recover a hyperspectral image (HSI) from a single RGB image and demonstrated good performance. In particular, attention mechanisms have further strengthened discriminative features, but most of them are learned by convolutions with limited receptive fields or require much computational cost, which hinders the function of attention modules. Furthermore, the performance of these deep learning methods is hampered by tackling multi-level features equally. To this end, in this paper, based on multiple lightweight densely residual modules, we propose a densely residual network with dual attention (DRN-DA), which utilizes advanced attention and adaptive fusion strategy for more efficient feature correlation learning and more powerful feature extraction. Specifically, an SE layer is applied to learn channel-wise dependencies, and dual downsampling spatial attention (DDSA) is developed to capture long-range spatial contextual information. All the intermediate-layer feature maps are adaptively fused. Experimental results on four data sets from the NTIRE 2018 and NTIRE 2020 Spectral Reconstruction Challenges demonstrate the superiority of the proposed DRN-DA over state-of-the-art methods (at least −6.19% and −1.43% on NTIRE 2018 “Clean” and “Real World” track, −6.85% and −5.30% on NTIRE 2020 “Clean” and “Real World” track) in terms of mean relative absolute error.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Densely Residual Network with Dual Attention for Hyperspectral Reconstruction from RGB Images

2022

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“…The surface spectral reflectance is known as the fingerprint of object colors ( Wang et al, 2020 ) and can characterize colors more accurately than RGB trichromatic information, enabling the replication and reproduction of color information. Therefore, obtaining accurate spectral information through spectral imaging is a hot topic in color science research.…”

Section: Introductionmentioning

confidence: 99%

“…The method ignores the spectral differences between samples. Wang proposed a two-time sequence weighting method considering the chromaticity and spectral error at the same time, and adaptively optimized the sample selection, which achieved good results ( Wang et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

An improved spectral estimation method based on color perception features of mobile phone camera

Liu

Wu²,

Liang³

et al. 2022

Front. Neurosci.

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We use the mobile phone camera as a new spectral imaging device to obtain raw responses of samples for spectral estimation and propose an improved sequential adaptive weighted spectral estimation method. First, we verify the linearity of the raw response of the cell phone camera and investigate its feasibility for spectral estimation experiments. Then, we propose a sequential adaptive spectral estimation method based on the CIE1976 L*a*b* (CIELAB) uniform color space color perception feature. The first stage of the method is to weight the training samples and perform the first spectral reflectance estimation by considering the Lab color space color perception features differences between samples, and the second stage is to adaptively select the locally optimal training samples and weight them by the first estimated root mean square error (RMSE), and perform the second spectral reconstruction. The novelty of the method is to weight the samples by using the sample in CIELAB uniform color space perception features to more accurately characterize the color difference. By comparing with several existing methods, the results show that the method has the best performance in both spectral error and chromaticity error. Finally, we apply this weighting strategy based on the CIELAB color space color perception feature to the existing method, and the spectral estimation performance is greatly improved compared with that before the application, which proves the effectiveness of this weighting method.

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“…The problem of estimating the surface-spectral reflectance from image data has a long history. So far, many methods have been proposed in a variety of fields, including color science, image science and technology, and computer vision (e.g., see [1][2][3][4][5]. When an imaging system observes the reflected light, called color signal [6], from object surfaces illuminated by a light source, the reflectance estimation problem always involves separating illumination and reflectance from the color signal.…”

Section: Introductionmentioning

confidence: 99%

Spectral-Reflectance Estimation under Multiple Light Sources

Tominaga

2021

cic

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We describe a comprehensive method for estimating the surface-spectral reflectance from the image data of objects acquired under multiple light sources. This study uses the objects made of an inhomogeneous dielectric material with specular highlights. A spectral camera is used as an imaging system. The overall appearance of objects in a scene results from the chromatic factors such as reflectance and illuminant and the shading terms such as surface geometry and position. We first describe the method of estimating the illuminant spectra of multiple light sources based on detecting highlights appearing on object surfaces. The highlight candidates are detected first, and then some appropriate highlight areas are interactively selected among the candidates. Next, we estimate the spectral reflectance from a wide area selected from an object's surface. The color signals observed from the selected area are described using the estimated illuminant spectra, the surfacespectral reflectance, and the shading terms. This estimation utilizes the fact that the definition domains of reflectance and shading terms are different in each other. We develop an iterative algorithm for estimating the reflectance and the shading terms in two steps repeatedly. Finally, the feasibility of the proposed method is confirmed in an experiment using everyday objects under the illumination environment with multiple light sources.

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Sequential adaptive estimation for spectral reflectance based on camera responses

Cited by 18 publications

References 23 publications

Densely Residual Network with Dual Attention for Hyperspectral Reconstruction from RGB Images

Densely Residual Network with Dual Attention for Hyperspectral Reconstruction from RGB Images

An improved spectral estimation method based on color perception features of mobile phone camera

Spectral-Reflectance Estimation under Multiple Light Sources

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