Digital Camera Imaging System Simulation

Chen, Junqing; Venkataraman, Kartik; Bakin, Dmitry; Rodricks, Brian G.; Gravelle, Robert; Rao, P. Srinivasa; Ni, Yongshen

doi:10.1109/ted.2009.2030995

Cited by 55 publications

(37 citation statements)

References 13 publications

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“…The data validity is ensured by avoiding any pixel with a maximum output, such as 1024 for a 10-bit sensor. This model has been used many applications such as in [7] , [8].However, this is not accurate enough when the sensor is in low illumination because the transistor leakage is too big to be neglected.…”

Section: State-of-the-artmentioning

confidence: 99%

Leakage Modeling and Flat Field Correction Algorithm for CMOS Hyper Spectral Imaging Sensor

Liu¹,

Mainali²,

Tackand³

et al. 2013

IJMO

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Abstract-In hyper spectral imaging applications, the light power reaching the image sensofr is greatly reduced compared to broadband color image sensors. Consequently, some typical algorithms, such as Flat Field Correction (FFC), are not guaranteed anymore to work in the same way as in their normal operation for broadband color imaging. This is caused by transistor leakage which cannot be neglected anymore. In this paper, we propose a mathematical leakage model based on the basic transistor theory to tag the validity of the sensor response. The model has been validated by comparing the simulation results to measurements of the pixel response of a hyper spectral imager. We also demonstrate that this leakage model is able to select the proper training set for a typical FFC algorithm.Index Terms-Leakage model, transistor leakage, CMOS imager, hyper spectral imaging, flat field correction.

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Section: State-of-the-artmentioning

confidence: 99%

Leakage Modeling and Flat Field Correction Algorithm for CMOS Hyper Spectral Imaging Sensor

Liu¹,

Mainali²,

Tackand³

et al. 2013

IJMO

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“…In studies of noise performance it is important to have simulated noise resembling what would be seen in real physical devices. This goal was achieved by using an internally developed digital camera imaging system simulation tool called the Camera and Scene Model (CSM) 6 . 1 shows the flowchart for generating scene-dependent noise masks using CSM and original raw images from a Canon EOS 1Ds Mark II camera.…”

Section: Noise Mask Generationmentioning

confidence: 99%

Development of a perceptually calibrated objective metric of noise

Keelan¹,

Jin²,

Prokushkin³

2011

SPIE Proceedings

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A system simulation model was used to create scene-dependent noise masks that reflect current performance of mobile phone cameras. Stimuli with different overall magnitudes of noise and with varying mixtures of red, green, blue, and luminance noises were included in the study. Eleven treatments in each of ten pictorial scenes were evaluated by twenty observers using the softcopy ruler method. In addition to determining the quality loss function in just noticeable differences (JNDs) for the average observer and scene, transformations for different combinations of observer sensitivity and scene susceptibility were derived. The psychophysical results were used to optimize an objective metric of isotropic noise based on system noise power spectra (NPS), which were integrated over a visual frequency weighting function to yield perceptually relevant variances and covariances in CIE L*a*b* space. Because the frequency weighting function is expressed in terms of cycles per degree at the retina, it accounts for display pixel size and viewing distance effects, so application-specific predictions can be made. Excellent results were obtained using only L* and a* variances and L*a* covariance, with relative weights of 100, 5, and 12, respectively. The positive a* weight suggests that the luminance (photopic) weighting is slightly narrow on the long wavelength side for predicting perceived noisiness. The L*a* covariance term, which is normally negative, reflects masking between L* and a* noise, as confirmed in informal evaluations. Test targets in linear sRGB and rendered L*a*b* spaces for each treatment are available at http://www.aptina.com/ImArch/ to enable other researchers to test metrics of their own design and calibrate them to JNDs of quality loss without performing additional observer experiments. Such JND-calibrated noise metrics are particularly valuable for comparing the impact of noise and other attributes, and for computing overall image quality.

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“…At each narrow spectrum band the experiment was repeated a few times to get reliable data. Please refer to [6] for details regarding the setup. The designed setup gives us enough equations to solve for the unknowns.…”

Section: Experiments Setupmentioning

confidence: 99%

“…For details on system simulation tool please refer to Ref. [6]. Three demosaic methods are compared, the simple bilinear interpolation, and two edgeaware algorithms, named as pattern recognition and anisotropic demosaic (both are Aptina internal algorithms).…”

Section: Sensor Mtf Estimationmentioning

confidence: 99%

Imaging sensor Modulation Transfer Function estimation

Chen¹,

Cao²,

Sun³

et al. 2010

2010 IEEE International Conference on Image Processing

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The Modulation Transfer Function (MTF) is an important quantitative indicator of imaging system resolving power. State of the art MTF measurement approaches utilize known targets and work quite well on pixels of size over 2.0 micron. While efforts have been taken towards reliable MTF measurement of smaller pixels, existing methods suffer from high complexities in measurement and analysis. The proposed approach originates from a linear camera model and decomposes the system to linear blocks and sampling operation. The sensor output signal is modeled as a linear combination of intended and unintended signal (noise plus crosstalk). The crosstalk itself is again modeled via a linear kernel that can be obtained based on data from a designed experiment. The sensor MTF is then estimated by combining the relevant elements together. The results are verified against these from a much more sophisticated setup [1] and show good alignment.

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Digital Camera Imaging System Simulation

Cited by 55 publications

References 13 publications

Leakage Modeling and Flat Field Correction Algorithm for CMOS Hyper Spectral Imaging Sensor

Leakage Modeling and Flat Field Correction Algorithm for CMOS Hyper Spectral Imaging Sensor

Development of a perceptually calibrated objective metric of noise

Imaging sensor Modulation Transfer Function estimation

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