Autofluorescence imaging(AFI) systems are widely used in the detection of precancerous lesions.Fluorescence images of precancerous tissue are usually red(R) or blue(B), so this kind of system has high requirement for ...Autofluorescence imaging(AFI) systems are widely used in the detection of precancerous lesions.Fluorescence images of precancerous tissue are usually red(R) or blue(B), so this kind of system has high requirement for colour recovery, especially in R and B channels. Besides, AFI system requires bulk data transmission with no time delay. Existing colour recovery algorithms focus more on green(G) channel, overlooking R and B channels. Although the state-of-art demosaicing algorithms can perform well in colour recovery, they often have high computational cost and high hardware requirements. We propose an efficient interpolation algorithm with low complexity to solve the problem. When calculating R and B channel values, we innovatively propose the diagonal direction to select the interpolation direction, and apply colour difference law to make full use of the correlation between colour channels. The experimental results show that the peak signal-to-noise ratios(PSNRs)of G, R and B channels reach 37.54, 37.40 and 38.22 dB, respectively, which shows good performance in recovery of R and B channels. In conclusion, the algorithm proposed in this paper can be used as an alternative to the existing demosaicing algorithms for AFI system.展开更多
Color Fourier single-pixel imaging(FSI)enables efficient spectral and spatial imaging.Here,we propose a Fourier single-pixel imaging scheme with a random color filter array(FSI-RCFA).The proposed method employs a rand...Color Fourier single-pixel imaging(FSI)enables efficient spectral and spatial imaging.Here,we propose a Fourier single-pixel imaging scheme with a random color filter array(FSI-RCFA).The proposed method employs a random color filter array(RCFA)to modulate Fourier patterns.A three-step phase-shifting technique reconstructs the Fourier spectrum,followed by an RCFA-based demosaicing algorithm to recover color images.Compared to traditional color FSI based on Bayer color filter array schemes(FSI-BCFA),our approach achieves superior separation between chrominance and luminance components in the frequency domain.Simulation results demonstrate that the FSI-RCFA method achieves a lower mean squared error(MSE),a higher peak signal-to-noise ratio(PSNR),and superior noise resistance compared to FSI-BCFA,while enabling direct single-channel pixel measurements for targeted applications such as agricultural defect detection.展开更多
Silicon-based digital cameras can record visible and near-infrared （NIR） information, in which the full color visible image （RGB） must be restored from color filter ar- ray （CFA） interpolation. In this paper, we...Silicon-based digital cameras can record visible and near-infrared （NIR） information, in which the full color visible image （RGB） must be restored from color filter ar- ray （CFA） interpolation. In this paper, we propose a uni- fied framework for CFA interpolation and visible/NIR image combination. To obtain a high quality color image, the tra- ditional color interpolation from raw CFA data is improved at each pixel, which is constrained by the corresponding monochromatic NIR image in gradient difference. The ex- periments indicate the effectiveness of this hybrid scheme to acquire joint color and NIR information in real-time, and show that this hybrid process can generate a better color im- age when compared to treating interpolation and fusion sep- arately.展开更多
In this paper, a joint multifocus image fusion and Bayer pattern image restoration algorithm for raw images of single-sensor colorimaging devices is proposed. Different from traditional fusion schemes, the raw Bayer p...In this paper, a joint multifocus image fusion and Bayer pattern image restoration algorithm for raw images of single-sensor colorimaging devices is proposed. Different from traditional fusion schemes, the raw Bayer pattern images are fused before colorrestoration. Therefore, the Bayer image restoration operation is only performed one time. Thus, the proposed algorithm is moreefficient than traditional fusion schemes. In detail, a clarity measurement of Bayer pattern image is defined for raw Bayer patternimages, and the fusion operator is performed on superpixels which provide powerful grouping cues of local image feature. Theraw images are merged with refined weight map to get the fused Bayer pattern image, which is restored by the demosaicingalgorithm to get the full resolution color image. Experimental results demonstrate that the proposed algorithm can obtain betterfused results with more natural appearance and fewer artifacts than the traditional algorithms.展开更多
基金the National Natural Science Foundation of China(Nos.61673271 and 81601631)the Shanghai Scientific Project(No.15441903100)the Postdoctoral Science Foundation of China(No.2016M601587)
文摘Autofluorescence imaging(AFI) systems are widely used in the detection of precancerous lesions.Fluorescence images of precancerous tissue are usually red(R) or blue(B), so this kind of system has high requirement for colour recovery, especially in R and B channels. Besides, AFI system requires bulk data transmission with no time delay. Existing colour recovery algorithms focus more on green(G) channel, overlooking R and B channels. Although the state-of-art demosaicing algorithms can perform well in colour recovery, they often have high computational cost and high hardware requirements. We propose an efficient interpolation algorithm with low complexity to solve the problem. When calculating R and B channel values, we innovatively propose the diagonal direction to select the interpolation direction, and apply colour difference law to make full use of the correlation between colour channels. The experimental results show that the peak signal-to-noise ratios(PSNRs)of G, R and B channels reach 37.54, 37.40 and 38.22 dB, respectively, which shows good performance in recovery of R and B channels. In conclusion, the algorithm proposed in this paper can be used as an alternative to the existing demosaicing algorithms for AFI system.
基金supported by the National Natural Science Foundation of China(Grant Nos.62001249 and62375140)。
文摘Color Fourier single-pixel imaging(FSI)enables efficient spectral and spatial imaging.Here,we propose a Fourier single-pixel imaging scheme with a random color filter array(FSI-RCFA).The proposed method employs a random color filter array(RCFA)to modulate Fourier patterns.A three-step phase-shifting technique reconstructs the Fourier spectrum,followed by an RCFA-based demosaicing algorithm to recover color images.Compared to traditional color FSI based on Bayer color filter array schemes(FSI-BCFA),our approach achieves superior separation between chrominance and luminance components in the frequency domain.Simulation results demonstrate that the FSI-RCFA method achieves a lower mean squared error(MSE),a higher peak signal-to-noise ratio(PSNR),and superior noise resistance compared to FSI-BCFA,while enabling direct single-channel pixel measurements for targeted applications such as agricultural defect detection.
文摘Silicon-based digital cameras can record visible and near-infrared （NIR） information, in which the full color visible image （RGB） must be restored from color filter ar- ray （CFA） interpolation. In this paper, we propose a uni- fied framework for CFA interpolation and visible/NIR image combination. To obtain a high quality color image, the tra- ditional color interpolation from raw CFA data is improved at each pixel, which is constrained by the corresponding monochromatic NIR image in gradient difference. The ex- periments indicate the effectiveness of this hybrid scheme to acquire joint color and NIR information in real-time, and show that this hybrid process can generate a better color im- age when compared to treating interpolation and fusion sep- arately.
文摘In this paper, a joint multifocus image fusion and Bayer pattern image restoration algorithm for raw images of single-sensor colorimaging devices is proposed. Different from traditional fusion schemes, the raw Bayer pattern images are fused before colorrestoration. Therefore, the Bayer image restoration operation is only performed one time. Thus, the proposed algorithm is moreefficient than traditional fusion schemes. In detail, a clarity measurement of Bayer pattern image is defined for raw Bayer patternimages, and the fusion operator is performed on superpixels which provide powerful grouping cues of local image feature. Theraw images are merged with refined weight map to get the fused Bayer pattern image, which is restored by the demosaicingalgorithm to get the full resolution color image. Experimental results demonstrate that the proposed algorithm can obtain betterfused results with more natural appearance and fewer artifacts than the traditional algorithms.
