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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.


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Proceedings Article
01 Sep 1996
TL;DR: A new method is described which overcomes the typical disadvantage of one channel noise suppression algorithms — the impossibility of noise estimation during speech sequence and is the combination of Wiener filtering and spectral subtraction.
Abstract: This paper describes a new method for one channel noise suppression system which overcomes the typical disadvantage of one channel noise suppression algorithms — the impossibility of noise estimation during speech sequence. Our method is the combination of Wiener filtering and spectral subtraction. The noise can be successfully updated even during the speech sequences and that is why there is no need of the voice activity detector.

44 citations

Proceedings ArticleDOI
28 May 1999
TL;DR: In this article, the conditions to safeguard the signal to noise ratio in the current image are detailed: there is an optimal number of dark and 'white' images to be averaged in order to keep their electronic and quantum noise negligible compared to that of the original image.
Abstract: Offset and gain corrections are indispensable to exploit images from large image sensors, because of the pixel to pixel variation in dark current and sensitivity. However, an inappropriate correction may be detrimental to the signal to noise ratio of the raw image. This is especially critical in X-ray imaging, where the quantum noise is filtered by the detector spatial response. The noise power spectrum (NPS) in the corrected image is a combination of the initial noise spectrum in the raw image (quantum noise and electronic noise) with the noise in the offset and gain images. The dark image noise power just adds up to the noise power in the current image. The noise in the gain image alters the noise of the current image in a more intricate way. This is illustrated by simulations and experimental measurements. The conditions to safeguard the signal to noise ratio in the current image are detailed: There is an optimal number of dark and 'white' images to be averaged in order to keep their electronic and quantum noise negligible compared to that of the current image. Real conditions often force trade-offs between the desirable large number of offset/gain images to be averaged and the time effectively assigned to such acquisitions. Furthermore, the residual noise spectrum in the gain images is dependent on dose, uniformity of irradiation, temperature and detector spatial response. In the appropriate conditions, the intrinsic signal to noise ratio of an image can be preserved by offset and gain correction. Nevertheless, at high dose, the gain correction unavoidably introduces some high frequency proportional noise which degrades the DQE.

44 citations

Proceedings Article
01 Jan 2007
TL;DR: A novel noise fading technique based on noise detection and median filtering, which prevents image blurring and is computationally simple, is proposed in this paper and outperforms all existing impulse-denoising schemes.

44 citations

Patent
10 Apr 2002
TL;DR: In this paper, a method for dark current subtraction which enables a dark frame to be reused for the task of dark current removal for multiple image frames is described. But the method is not suitable for the case of image frames and it requires the dark frame is reused by scaling it according to changes in the dark current levels associated with the image frames.
Abstract: A method for dark current subtraction which enables a dark frame to be reused for dark current subtraction for multiple image frames. The dark frame is reused by scaling it according to changes in the dark current levels associated with the dark frame and the image frames.

43 citations

Journal ArticleDOI
TL;DR: The proposed method outperforms all standard algorithms for the reduction of impulsive noise in color images because it filters out the noise component while adapting itself to the local image structures.
Abstract: A new approach to the problem of impulsive-noise reduction for color images is introduced. The major advantage of the technique is that it filters out the noise component while adapting itself to the local image structures. In this way the algorithm is able to eliminate strong impulsive noise while preserving edges and fine image details. As the algorithm is a fuzzy modification of the commonly used vector median operator, it is very fast and easy to implement. Our results show that the proposed method outperforms all standard algorithms for the reduction of impulsive noise in color images. © 2001 Society of Photo-Optical Instrumenta- tion Engineers. (DOI: 10.1117/1.1367347)

43 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20238
202221
20213
20202
20192
20187