Abstract
A probabilistic method to remove the random telegraph signal (RTS) noise and to increase the signal level is proposed, and was verified by simulation based on measured real sensor noise. Although semi-photon-counting-level (SPCL) ultra-low noise complementary-metal-oxide-semiconductor (CMOS) image sensors (CISs) with high conversion gain pixels have emerged, they still suffer from huge RTS noise, which is inherent to the CISs. The proposed method utilizes a multi-aperture (MA) camera that is composed of multiple sets of an SPCL CIS and a moderately fast and compact imaging lens to emulate a very fast single lens. Due to the redundancy of the MA camera, the RTS noise is removed by the maximum likelihood estimation where noise characteristics are modeled by the probability density distribution. In the proposed method, the photon shot noise is also relatively reduced because of the averaging effect, where the pixel values of all the multiple apertures are considered. An extremely low-light condition that the maximum number of electrons per aperture was the only 2 was simulated. PSNRs of a test image for simple averaging, selective averaging (our previous method), and the proposed method were 11.92 dB, 11.61 dB, and 13.14 dB, respectively. The selective averaging, which can remove RTS noise, was worse than the simple averaging because it ignores the pixels with RTS noise and photon shot noise was less improved. The simulation results showed that the proposed method provided the best noise reduction performance.
Highlights
Low light imaging is required in various fields, such as astronomical observation [1], bio-imaging [2], and surveillance [3,4], where high sensitivity cameras are used
One pixel of a synthesized image is composed of multiple pixels from different image sensors, and the pixels that generate random telegraph signal (RTS) noise are adaptively removed based on the amount of the calculated synthetic noise from the noise measured in the dark condition
Because a part of pixel values was ignored in the selective averaging, in this simulation, the penalty for less improvement of photon shot noise was more significant than the benefit by ignoring the RTS noise pixels
Summary
Low light imaging is required in various fields, such as astronomical observation [1], bio-imaging [2], and surveillance [3,4], where high sensitivity cameras are used. One pixel of a synthesized image is composed of multiple pixels from different image sensors, and the pixels that generate RTS noise are adaptively removed based on the amount of the calculated synthetic noise from the noise measured in the dark condition. Note that the synthetic sensor noise is evaluated pixel by pixel Application of this method to color imaging [26] and disparity correction with noisy multi-aperture images [30] has been studied. It has been proven that the selective averaging method effectively removed RTS noise, the photon shot noise did not decrease efficiently because the number of pixels that were used in reproduction decreased. Because the MA camera provides multiple pixel values for one pixel in a reproduced image, the RTS noise level can be estimated and the noise is removed.
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