The study of key technology on spectral reflectance reconstruction based on the algorithm of adaptive compressive sensing

Zhang, Leihong; Liang, Dong; Li, Bei; Yi, Kechu; Pan, Zilan; Zhang, Dawei; Ma, Xiuhua

doi:10.1088/1054-660x/26/4/045201

Cited by 4 publications

(7 citation statements)

References 13 publications

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“…M is the measurement matrix between y and r. In the method of spectral reflectance reconstruction based on a single pixel detector, the image has been modulated in space and spectrum, respectively. Thus, the relationship of the values obtained by the single pixel detector and the spectral reflectance of the image is shown in equation (7). In equation (7), H is the modulation matrix of DMD, which is a p × q matrix.…”

Section: Spectral Reflectance Reconstruction Methods Based On a Singl...mentioning

confidence: 99%

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Study on the key technology of spectral reflectance reconstruction based on a single pixel detector

Hao-jun

Zhang

et al. 2017

Laser Phys. Lett.

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In order to realize an accurate reconstruction of images, this article explores image reconstruction from a spectrum point of view. Combined with a principal component analysis method and compressed sensing algorithm, this article puts forward a spectral reflectance reconstruction algorithm based on a single pixel detector, and compares the different modulation matrixes for DMD in the experiment. From subjective evaluation and three objective evaluation methods: color difference, peak signal-to-noise ratio and structural similarity, we compare and analyze the reconstruction effects of two modulation methods: random modulation and Hadamard modulation. The experimental results show that the color difference of the spectral reflectance reconstruction method of a single pixel detector based on random modulation is smaller, the similarity of the structure is higher, and the peak signal-to-noise ratio is greater than Hadamard modulation. To sum up, the spectral reflectance reconstruction algorithm of a single pixel detector based on random modulation is better than Hadamard modulation, which is more conducive to an accurate reconstruction of spectral reflectance.

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Section: Spectral Reflectance Reconstruction Methods Based On a Singl...mentioning

confidence: 99%

“…The compressed sensing algorithm [6][7][8] is a new image reconstruction algorithm. It is applied to the reconstruction of spectral images, which can achieve the effect of an accurate reconstruction of spectral images.…”

Section: Compressed Sensing Algorithmmentioning

confidence: 99%

Study on the key technology of spectral reflectance reconstruction based on a single pixel detector

Hao-jun

Zhang

et al. 2017

Laser Phys. Lett.

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“…[7], the spectral reflectance reconstruction based on the algorithm of adaptive compressive sensing has been suggested, which takes into account the representation and the relevance of sample selection and so improves clearly the reconstruction accuracy. However, both of the methods [6,7] require huge training samples to obtain a so-called 'average transformation matrix' for reconstructing spectral reflectance with specific light sources. As a result, the reconstruction precision is notably affected by a set of training samples and a light source chosen.…”

Section: Spectral Reflectance Recovery From a White-balanced Rgb Imagmentioning

confidence: 99%

“…can be obtained by the least square method as min 2 2 Qu r  . Then the spectral reflectance can be obtained [6,7]:…”

Section: Spectral Reflectance Reconstruction Based On Pseudo-inverse mentioning

confidence: 99%

Spectral reflectance recovery from a white-balanced RGB image based on the algorithm of compressive sensing

Zhang¹,

Liang²,

Li³

et al. 2016

Ukr. J. Phys. Opt.

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In this work we study the methods for reconstructing spectral reflectances of images. To reproduce the spectral reflectance more accurately and the colours more realistically, we obtain a so-called average transformation matrix, using a series of different modulated light sources to illuminate a standard whiteboard. For eliminating the influence of those light sources on RGB images, we first use a white-balance algorithm to standardize the scene information, thus obtaining a uniform RGB image with different sources. Then we map the RGB image onto the spectral reflectance basing on a compressive sensing algorithm and the principal component analysis and, finally, reconstruct the spectral reflectance of the testing sample. We compare the reconstruction accuracies of our compressive sensing algorithm based on white-balance calibration with the results derived using a pseudo-inverse method and a traditional compressive sensing algorithm. Our simulation results show that, under the same conditions of reconstruction, the residual errors and the colour difference resulting from our improved algorithm are less than those produced by the other two algorithms. In other words, the reconstruction algorithm suggested by us outperforms the other methods and can provide better performance of image colour reproduction.

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“…In particular, application of the CS in the spectral-reflectance reconstruction improves the reconstruction accuracy so that the image can be restored more accurately [11,12]. To improve the reconstruction accuracy and reduce workload, an adaptive CS has been used in the work [13] to reconstruct the spectral reflectance. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Studies on the key methods for compressive ghost-image tracking based on background subtraction

Zhang¹,

Yi²,

Li³

et al. 2017

Ukr. J. Phys. Opt.

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Abstract. Efficient object tracking represents a technology important for many vision applications. It is known that ghost imaging (GI) has a great potential if compared with a standard imaging and solves many problems in case if the common object tracking cannot be carried out. Here we show how the techniques of compressive GI and background subtraction can achieve object tracking. First, object information is captured with the GI. A characteristic measured for an object is obtained by subtracting background in the compressed domain. This characteristic uses compressive sensing to reconstruct the object image. Then the object image is projection-positioned to obtain the corresponding centroid coordinates. At last, the object trajectory is recovered with a polynomial fit, thus providing successful object tracking. Our simulation experiments suggest that the technique can track objects accurately under condition of low sampling ratios. Moreover, it decreases drastically the number of measurements needed for reconstruction and improves the tracking efficiency.

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The study of key technology on spectral reflectance reconstruction based on the algorithm of adaptive compressive sensing

Cited by 4 publications

References 13 publications

Study on the key technology of spectral reflectance reconstruction based on a single pixel detector

Study on the key technology of spectral reflectance reconstruction based on a single pixel detector

Spectral reflectance recovery from a white-balanced RGB image based on the algorithm of compressive sensing

Studies on the key methods for compressive ghost-image tracking based on background subtraction

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