A Fixed-Pattern Noise Correction Method Based on Gray Value Compensation for TDI CMOS Image Sensor

Liu, Zhenwang; Xu, Jiangtao; Wang, Xinlei; Nie, Kaiming; Jin, Weimin

doi:10.3390/s150923496

Cited by 17 publications

(7 citation statements)

References 18 publications

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“…In this paper, the experimental results are evaluated by subjective and objective indexes. The subjective indexes include the image visual effect and mean curve [19]. The objective index includes the standard deviation.…”

Section: Resultsmentioning

confidence: 99%

The Analysis and Suppressing of Non-Uniformity in a High-Speed Spike-Based Image Sensor

Gao

Wang

Nie

et al. 2018

Sensors

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In this paper, the non-ideal factors, which include spatial noise and temporal noise, are analyzed and suppressed in the high-speed spike-based image sensor, which combines the high-speed scanning sequential format with the method that uses the interspike time interval to indicate the scene information. In this imager, spatial noise contains device mismatch, which results in photo response non-uniformity (PRNU) and the non-uniformity of dark current. By multiplying the measured coefficient matrix the photo response non-uniformity is suppressed, and the non-uniformity of dark current is suppressed by correcting the interspike time interval based on the time interval of dark current. The temporal noise is composed of the shot noise and thermal noise. This kind of noise can be eliminated when using the spike frequency to restore the image. The experimental results show that, based on the spike frequency method, the standard deviation of the image decreases from 18.4792 to 0.5683 in the uniform bright light by using the calibration algorithm. While in the relatively uniform dark condition, the standard deviation decreases from 1.5812 to 0.4516. Based on interspike time interval method, because of time mismatch and temporal noise, the standard deviation of the image changes from 27.4252 to 27.4977 in the uniform bright light by using the calibration algorithm. While in the uniform dark condition, the standard deviation decreases from 2.361 to 0.3678.

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Section: Resultsmentioning

confidence: 99%

The Analysis and Suppressing of Non-Uniformity in a High-Speed Spike-Based Image Sensor

Gao

Wang

Nie

et al. 2018

Sensors

Self Cite

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“…At present, the imaging performance of CGI is nowhere near image sensors using detector arrays, especially silicon-based charge-coupled devices and complementary metal oxide semiconductors [32,33]. Beside the fact that the research and manufacture of detector arrays are intensively invested in due to the global market demands, sophisticated calibration procedures, such as dark current noise suppression and pixel-response non-uniformity compensation, are applied before the image sensors are put into actual use [34][35][36][37][38][39]. Therefore, it would be beneficial to investigate the same concept to improve the image quality of CGI.…”

Section: Ofmentioning

confidence: 99%

Illumination Calibration for Computational Ghost Imaging

et al. 2021

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We propose a fast calibration method to compensate the non-uniform illumination in computational ghost imaging. Inspired by a similar procedure to calibrate pixel response differences for detector arrays in conventional digital cameras, the proposed method acquires one image of an all-white paper to determine the non-uniformity of the illumination and uses the information to calibrate any further reconstructed images under the same illumination. The numerical and experimental results are in a good agreement, and the experimental results showed that the root mean square error of the reconstructed image was reduced by 79.94% after the calibration.

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“…Currently, the readout circuit of an uncooled infrared imaging system mostly adopts the CMOS architecture. Different from the CCD structure in which all pixels share a common amplifier, the pixels in each column of the CMOS readout circuit share an amplifier [27]. As amplifiers have different electronic properties, the columns of the CMOS readout circuit are non-uniform and thus appear as stripes in the image.…”

Section: Small Window Column Equalizationmentioning

confidence: 99%

Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization

et al. 2019

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In the uncooled infrared imaging systems, owing to the non-uniformity of the amplifier in the readout circuit, the infrared image has obvious stripe noise, which greatly affects its quality. In this study, the generation mechanism of stripe noise is analyzed, and a new stripe correction algorithm based on wavelet analysis and gradient equalization is proposed, according to the single-direction distribution of the fixed image noise of infrared focal plane array. The raw infrared image is transformed by a wavelet transform, and the cumulative histogram of the vertical component is convolved by a Gaussian operator with a one-dimensional matrix, in order to achieve gradient equalization in the horizontal direction. In addition, the stripe noise is further separated from the edge texture by a guided filter. The algorithm is verified by simulating noised image and real infrared image, and the comparison experiment and qualitative and quantitative analysis with the current advanced algorithm show that the correction result of the algorithm in this paper is not only mild in visual effect, but also that the structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) indexes can get the best result. It is shown that this algorithm can effectively remove stripe noise without losing details, and the correction performance of this method is better than the most advanced method.

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A Fixed-Pattern Noise Correction Method Based on Gray Value Compensation for TDI CMOS Image Sensor

Cited by 17 publications

References 18 publications

The Analysis and Suppressing of Non-Uniformity in a High-Speed Spike-Based Image Sensor

The Analysis and Suppressing of Non-Uniformity in a High-Speed Spike-Based Image Sensor

Illumination Calibration for Computational Ghost Imaging

Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization

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