On the Optimization of Regression-Based Spectral Reconstruction

Lin, Yi‐Tun; Finlayson, Graham D.

doi:10.3390/s21165586

Cited by 11 publications

(24 citation statements)

References 84 publications

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“…We also compared three recent RGB-based spectral reconstruction methods, namely, Polynomial methods [ 18 , 26 ], RBF (Radial Basis Function) network method [ 17 ], and Gaussian Process method [ 20 ]. Sparse coding [ 1 ] and deep learning [ 19 ] methods require a lot more training samples and they are not included in this work.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Improving Generalizability of Spectral Reflectance Reconstruction Using L1-Norm Penalization

Yao

Xin

2023

Sensors

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Spectral reflectance reconstruction for multispectral images (such as Weiner estimation) may perform sub-optimally when the object being measured has a texture that is not in the training set. The accuracy of the reconstruction is significantly lower without training samples. We propose an improved reflectance reconstruction method based on L1-norm penalization to solve this issue. Using L1-norm, our method can provide the transformation matrix with the favorable sparse property, which can help to achieve better results when measuring the unseen samples. We verify the proposed method by reconstructing spectral reflection for four types of materials (cotton, paper, polyester, and nylon) captured by a multispectral imaging system. Each of the materials has its texture and there are 204 samples in each of the materials/textures in the experiments. The experimental results show that when the texture is not included in the training dataset, L1-norm can achieve better results compared with existing methods using colorimetric measure (i.e., color difference) and shows consistent accuracy across four kinds of materials.

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Section: Experiments and Resultsmentioning

confidence: 99%

Improving Generalizability of Spectral Reflectance Reconstruction Using L1-Norm Penalization

Yao

Xin

2023

Sensors

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“…Despite in the literature DNN type SR algorithms work better (but not much better 32,33 ) than regression algorithms for seen and expected images (e.g., the ICVL datasets), our experiments show that regression‐based algorithms for spectral reconstruction work as well as their DNN counterparts for the following four scenarios: training with and testing on the SFU's MW images, training with and testing on the SFU's RMW images, training with the original ICVL hyperspectral images and testing on the ICVL's RMW set, and training with and testing on the ICVL's RMW sets. …”

Section: Introductionmentioning

confidence: 94%

An investigation on worst‐case spectral reconstruction from RGB images via Radiance Mondrian World assumption

Lin

Finlayson

2023

Color Research & Application

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Spectral reconstruction (SR) algorithms recover hyperspectral measurements from RGB camera responses. Statistical models at different levels of complexity are used to solve the SR problem-from the simplest closed-form regression, to sparse coding, to the complex deep neural networks (DNN). Recently, these methods were benchmarked based on the mean performance of the models and on a fixed set of real-world scenes, suggesting that more complex (more non-linear) models generally deliver better SR. In this paper, we investigate the relative performances of these models in terms of a real-world worst-case imaging condition called the Radiance Mondrian World (RMW) assumption. Under the RMW, testing hyperspectral images are composed of randomlyarranged and overlapping rectangular patches, where each patch is filled with one random radiance spectrum uniformly sampled from the convex closure of all natural radiances (i.e., all spectra in the concerned hyperspectral image dataset). Surprisingly, we show that all compared algorithms-regardless of their model complexity-degrade to broadly the same level of performance on our RMW testing set. Further, by retraining all models with an RMW training set, we show that increasing model complexity also does not help learning better SR mappings from the RMW images. That is, using simple regression is as good as using a DNN. This similarity of performance is also shown to hold for images adhering to the conventional Mondrian World assumption (random reflectances lit by a single, per scene, randomly selected light source).

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“…Methods for estimating the spectral reflectance from camera signals are critical for improving measurement accuracy and detection speed. The methods can be based on basis spectra [ 17 , 18 , 19 , 20 ], Wiener estimation [ 14 , 21 , 22 ], regression [ 23 , 24 , 25 , 26 , 27 , 28 ], and interpolation [ 29 , 30 , 31 , 32 , 33 , 34 ]. The basis-spectrum methods assume that the target spectrum to be reconstructed is a linear combination of the basis spectra derived from training samples.…”

Section: Introductionmentioning

confidence: 99%

“…The spectral reflectance image is reconstructed pixel-by-pixel using the methods in [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 29 , 30 , 31 , 32 , 33 , 34 ], i.e., the spectrum of a pixel is reconstructed from the camera signals of the pixel. For example, the authors of [ 32 ] showed spectral reflectance images reconstructed using the LUT method.…”

Section: Introductionmentioning

confidence: 99%

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Irradiance Independent Spectrum Reconstruction from Camera Signals Using the Interpolation Method

Wen

Hsu

et al. 2022

Sensors

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The spectrum of light captured by a camera can be reconstructed using the interpolation method. The reconstructed spectrum is a linear combination of the reference spectra, where the weighting coefficients are calculated from the signals of the pixel and the reference samples by interpolation. This method is known as the look-up table (LUT) method. It is irradiance-dependent due to the dependence of the reconstructed spectrum shape on the sample irradiance. Since the irradiance can vary in field applications, an irradiance-independent LUT (II-LUT) method is required to recover spectral reflectance. This paper proposes an II-LUT method to interpolate the spectrum in the normalized signal space. Munsell color chips irradiated with D65 were used as samples. Example cameras are a tricolor camera and a quadcolor camera. Results show that the proposed method can achieve the irradiance independent spectrum reconstruction and computation time saving at the expense of the recovered spectral reflectance error. Considering that the irradiance variation will introduce additional errors, the actual mean error using the II-LUT method might be smaller than that of the ID-LUT method. It is also shown that the proposed method outperformed the weighted principal component analysis method in both accuracy and computation speed.

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On the Optimization of Regression-Based Spectral Reconstruction

Cited by 11 publications

References 84 publications

Improving Generalizability of Spectral Reflectance Reconstruction Using L1-Norm Penalization

Improving Generalizability of Spectral Reflectance Reconstruction Using L1-Norm Penalization

An investigation on worst‐case spectral reconstruction from RGB images via Radiance Mondrian World assumption

Irradiance Independent Spectrum Reconstruction from Camera Signals Using the Interpolation Method

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