Study of V/Q Non-Linearity in Scientific CMOS Sensor

Li, Junlin; Guo, Yongfei; Taiji, Lan

doi:10.14257/ijgdc.2016.9.6.27

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…Hui Tian showed the evidence that the nonlinearity of an image sensor actually improves SNR at high illumination in [10]. Besides, Jun Lin analyzed the relationship between the nonlinearity and modulation transfer function (MTF) in [11]. Tradeoffs exist among the performances of noise, nonlinearity, and MTF for image sensors.…”

Section: Model Verificationmentioning

confidence: 99%

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Linearity analysis of a CMOS image sensor

Wang¹,

Theuwissen²

2017

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In this paper, we analyze the causes of the nonlinearity of a voltage-mode CMOS image sensor, including a theoretical derivation and a numerical simulation. A prototype chip designed in a 0.18 µm 1-poly 4-metal CMOS process technology is implemented to verify this analysis. The pixel array is 160 × 80 with a pitch of 15 µm, and it contains dozens of groups of pixels that have different design parameters. From the measurement results, we confirmed these factors affecting the linearity and can give guidance for a future design to realize a high linearity CMOS image sensor.

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Section: Model Verificationmentioning

confidence: 99%

“…W lateral is shown in Equation (11). φ BSW is the built-in potential of the sidewalls, and MJSW is the junction grading coefficients of the side-walls.…”

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confidence: 99%

Linearity analysis of a CMOS image sensor

Wang¹,

Theuwissen²

2017

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“…While raw sensor image intensity is often assumed to be ideally linear with the number of generated electrons; in reality, non-linearities may occur at different stages during the procedure of conversion from electrons to digital units (DUs) [69], [70]. More interestingly, there are image sensors which are intentionally designed to obtain a theoretical non-linear response of the form of a power function [71], [72].…”

Section: C) Noise Model For Multi-iso Sensormentioning

confidence: 99%

Compressive HDR Light Field Imaging Using a Single Multi-ISO Sensor

Miandji¹,

Nguyen²,

Hajisharif³

et al. 2021

IEEE Trans. Comput. Imaging

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In this paper, we propose a new design for single sensor compressive HDR light field cameras, combining multi-ISO photography with coded mask acquisition, placed in a compressive sensing framework. The proposed camera model is based on a main lens, a multi-ISO sensor and a coded mask located in the optical path between the main lens and the sensor that projects the coded spatio-angular information of the light field onto the 2D sensor. The model encompasses different acquisition scenarios with different ISO patterns and gains. Moreover, we assume that the sensor has a built-in color filter array (CFA), making our design more suitable for consumer-level cameras. We propose a reconstruction algorithm to jointly perform color demosaicing, light field angular information recovery, HDR reconstruction, and denoising from the multi-ISO measurements formed on the sensor. This is achieved by enabling the sparse representation of HDR light fields using an overcomplete HDR dictionary. We also provide two HDR light field data sets: one synthetic data set created using the Blender rendering software with two baselines, and a real light field data set created from the fusion of multi-exposure low dynamic range (LDR) images captured using a Lytro Illum light field camera. Experimental results show that, with a sampling rate as low as 2.67%, using two shots, our proposed method yields a higher light field reconstruction quality compared to the fusion of multiple LDR light fields captured with different exposures, and with the fusion of multiple LDR light fields captured with different ISO settings.

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