Modeling and Characterizing Optical CMOS Sensors for Biomedical Low-Intensity Light Detection

Kamrani, Ehsan; Hamady, Mohamad; Lesage, Frédéric

doi:10.1109/sbec.2013.50

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Low-light-level (LLL) imaging has a wide range of applications in, such as biomedical imaging [1], scene perception and intelligent monitoring at night, and military target recognition [2]. But, when the light signal is weak, the effective information will be submerged in noise and the captured image becomes almost invisible.…”

Section: Introductionmentioning

confidence: 99%

Local Sparse Structure Denoising for Low-Light-Level Image

Han

Yue

Zhang

et al. 2015

IEEE Trans. on Image Process.

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Sparse and redundant representations perform well in image denoising. However, sparsity-based methods fail to denoise low-light-level (LLL) images because of heavy and complex noise. They consider sparsity on image patches independently and tend to lose the texture structures. To suppress noises and maintain textures simultaneously, it is necessary to embed noise invariant features into the sparse decomposition process. We, therefore, used a local structure preserving sparse coding (LSPSc) formulation to explore the local sparse structures (both the sparsity and local structure) in image. It was found that, with the introduction of spatial local structure constraint into the general sparse coding algorithm, LSPSc could improve the robustness of sparse representation for patches in serious noise. We further used a kernel LSPSc (K-LSPSc) formulation, which extends LSPSc into the kernel space to weaken the influence of linear structure constraint in nonlinear data. Based on the robust LSPSc and K-LSPSc algorithms, we constructed a local sparse structure denoising (LSSD) model for LLL images, which was demonstrated to give high performance in the natural LLL images denoising, indicating that both the LSPSc- and K-LSPSc-based LSSD models have the stable property of noise inhibition and texture details preservation.

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Section: Introductionmentioning

confidence: 99%

Local Sparse Structure Denoising for Low-Light-Level Image

Han

Yue

Zhang

et al. 2015

IEEE Trans. on Image Process.

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“…Nowadays low-light-level (LLL) image detection techniques have developed rapidly, but restrictions of the detected environment have increased as well thus call for a low-cost, accurate and fast-measured solution which can be used for more applications such as biomedical imaging [1], military target recognition, astronomy observation, and fluorescence imaging [2]. In fact, the easiest way to solve this problem is to use a light to illuminate the target directly, but sometimes this method is unachievable due to many limits.…”

Section: Introductionmentioning

confidence: 99%

Improved method on image detection at low light level using a sinusoidal-shaped-function signal

Zhao

Zhou

et al. 2015

Journal of Modern Optics

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This study proposes an improved method which employs the shaped-function technology and Discrete Fourier Series Transform (DFST)-based algorithm to improve the subjective impression at low light level. A sinusoidal wave light signal which is generated in the low frequency range plays the role of a shaped-function signal. The basic procedure of the proposed method is that an LED with a time-varying (sinusoidal) beam is used to illuminate the image sensor evenly. Then, a DFST-based algorithm is employed to remove the sinusoidal wave signal from the captured images for restoring the low-light-level image signal with a low gray resolution. The main purpose of this method was to improve gray-level resolution and signal-to-noise ratio of the acquired image and process the image data in real time by sliding the window. The derivation processes and experiments verify that the improved method not only can reveal a better result than the algorithms we have proposed before, but also have a better performance on the imaging speed.

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Modeling and Characterizing Optical CMOS Sensors for Biomedical Low-Intensity Light Detection

Cited by 2 publications

References 4 publications

Local Sparse Structure Denoising for Low-Light-Level Image

Local Sparse Structure Denoising for Low-Light-Level Image

Improved method on image detection at low light level using a sinusoidal-shaped-function signal

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