Dark-frame subtraction

About: Dark-frame subtraction is a research topic. Over the lifetime, 1216 publications have been published within this topic receiving 20763 citations.

Staring Mosaic Sensor Noise Sources

Abstract: Four noise sources occurring in an earth-viewing staring mosaic sensor mounted in a satellite are discussed. The four noise sources are (1) the internal system noise (detector and CCD), (2) the photon noise due to the background radiance level, (3) the noise due to background spatial characteristics scanned by an instability of the satellite, and (4) the noise due to the background temporal variations caused by cloud movement, solar movement, and scintillation. These four noise sources are affected by the particular sensor and their effect at the output of a sensor normally requires extensive calculations. A method of estimating bounds for each noise is demonstrated. This is done by approximating the actual functions by means of log-amplitude (Bode) plots.

An improved spectral subtraction method

Abstract: Audio is always being affected by outside noise during the communications. Conventional spectral subtraction (CSS) is widely used due to its characteristic of low computational complexity, high real-time and easy to achieve. But its fatal flaw is that the de-noised signals contain a great deal of "music noise". The paper aims to reduce "music noise" as much as possible. Voice Activity Detection (VAD) is used to detect the starting and ending of the audio, so we use silent segment to estimate noise spectrum exactly. Furthermore, it introduces spectral decay factor to estimate noise effectively. Finally, some additional de-noising modules, such as smooth processing, threshold calculation and music noise removing, are added to the system in order to make system work stability. We use segment SNR as a evaluation of de-noising effect. Experiment results dedicate its flexibility.

Image enhancement for extremely low light conditions

Abstract: In this paper, a novel methodology is proposed for contrast enhancement and noise reduction in very noisy data with low dynamic range on images captured by surveillance camera under extremely low light condition. For the initial noise reduction, a motion adaptive temporal filtering based on the Kalman filter is employed. Then, the denoised image is first inverted and subsequently dehazed as a tone mapping to enhance the visibility based on the observation that the inverted low light image presents quite similar characteristics to hazy image. Finally, the remaining noise is removed using the Non-local means (NLM) denoising step. The overall approach essentially transforms very dark images progressively into more visible form and effectively reduces the high intensity noise generated by the tone mapping process. From the experimental results, effectiveness of the proposed method is validated by comparing with the most recent and leading conventional method.

Chroma noise reduction in DCT domain using soft-thresholding

Abstract: The chroma noise effect seriously reduces the quality of digital images and videos, especially if they are acquired in low-light conditions. This paper describes the DCT-CNR (Discrete Cosine Transform-Chroma Noise Reduction), an efficient chroma noise reduction algorithm based on soft-thresholding. It reduces the contribution of the DCT coefficients having highest probability to be corrupted by noise and preserves the ones corresponding to the details of the image. Experiments show that the proposed method achieves good results with low computational and hardware resources requirements.