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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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Journal ArticleDOI
01 Jun 2003
TL;DR: Initial physical and psycho-physical evaluations of the noise of high resolution LCDs demonstrate that spatial noise is the dominant noise in all LCDs, while temporal noise is insignificant and plays only a minor part.
Abstract: This paper presents the results of initial physical and psycho-physical evaluations of the noise of high resolution LCDs. Five LCDs were involved, having four different pixel structures. Spatial as well as temporal noise was physically measured with the aid of a high-performance CCD camera. Human contrast sensitivity in the presence of spatial noise was determined psycho-physically using square-wave patterns stimuli as well as square stimuli. The results demonstrate that spatial noise is the dominant noise in all LCDs, while temporal noise is insignificant and plays only a minor part. The magnitude of spatial noise of LCDs is in the range between that of CRTs with a P104 and that of CRTs with a P45. Of particular importance with respect to LCD noise is the contribution of the pixel structure to the Noise Power Spectrum, which shows up as sharp spikes at spatial frequencies beyond the LCDs' Nyquist frequency. Much more work is necessary to understand the impact of spatial noise on the diagnosis of malignant abnormalities like micro-calcifications.

6 citations

Journal ArticleDOI
TL;DR: A spot centroiding algorithm immune from noise pixels is proposed to improve the existing centroided algorithms that can not process images with discrete noise pixels directly, and it is achieved by a Field Programmable Gate Array (FPGA).
Abstract: A spot centroiding algorithm immune from noise pixels is proposed to improve the existing centroiding algorithms that can not process images with discrete noise pixels directly, and it is achieved by a Field Programmable Gate Array(FPGA).This algorithm does not depend on noise pre-process or post-process to eliminate noise pixels. Firstly,the algorithm marks the background pixel,noise pixel and the spot pixel,respectively,then each pixel is marked after it is compared with the neighbouring one.At the same time,centroid parameters that belong to the same spot are accumulated,while the real noise pixel is bypassed.Compared with currently existing algorithms,this method takes full advantage of the parallel processing ability of the FPGA.It can extract spot centroid coordinates and eliminate the noise pixel simultaneously when the image pixel is output,the pre-process image is not needed and the storage space is saved.This algorithm is especially suitable for processing the spot image with discrete and high-brightness noises caused by long exposure time.

6 citations

Journal ArticleDOI
TL;DR: In this paper, a spectral subtraction algorithm is proposed for reducing colored noise from noise corrupted speech, where the spectrum is divided into frequency sub-bands based on a nonlinear multiband bark scale and a psychoacoustically motivated weighting filter is included to eliminate residual musical noise.
Abstract: The noise signal does not affect uniformly the speech signal over the whole spectrum isn the case of colored noise. In order to deal with speech improvement in such situations a new spectral subtraction algorithm is proposed for reducing colored noise from noise corrupted speech. The spectrum is divided into frequency sub-bands based on a nonlinear multiband bark scale. For each sub-band, the noise corrupted speech power in past and present time frames is compared to statistics of the noise power to improve the determination of the presence or absence of speech. During the subtraction process, a larger proportion of noise is removed from sub-bands that do not contain speech. For sub-bands that contain speech, a function is developed which allows for the removal of less noise during relatively low amplitude speech and more noise during relatively high amplitude speech .Further the performance of the spectral subtraction is improved by formulating process without neglecting the cross correlation between the speech signal and background noise. Residual noise can be masked by exploiting the masking properties of the human auditory system. In the proposed method subtraction parameters are adaptively adjusted using noise masking threshold. A psychoacoustically motivated weighting filter was included to eliminate residual musical noise. Experimental results show that the algorithm removes more colored noise without removing the relatively low amplitude speech at the beginning and ending of words.

6 citations

01 Jan 2005
TL;DR: In this article, it was shown that there is no shot noise in metallic resistors, just thermal noise and a frequency-dependent noise known as 1/f noise, which is well known to occur in solid-state devices such as tunnel junctions, Schottky barrier diodes and p-n junctions.
Abstract: Shot noise, the time-dependent fluctuations in electrical current caused by the discreteness of the electron charge, is well known to occur in solid-state devices, such as tunnel junctions, Schottky barrier diodes and p-n junctions. Most textbooks on electronic devices will tell you that there is no shot noise in metallic resistors, just thermal noise and a frequency-dependent noise known as 1/f noise. However, our basic knowledge of electrical conduction in small devices has advanced to the stage where it is clear that this notion does not hold.

6 citations

Proceedings ArticleDOI
S.Y. Koay1, Abdul Rahman Ramli1, Y.P. Lew1, V. Prakash1, R. Ali1 
07 Nov 2002
TL;DR: The continuous steps presented in this paper are thresholding, noise removal and motion region estimation of output image obtained from image subtraction.
Abstract: This paper presents the steps involved in processing the output image obtained from image subtraction. A Web camera or Web cam is used as a video clip capture device and MATLAB version 6.01 with Image Processing Toolbox is used as the analysis software. Image frames extracted from the video clips undergo image subtraction for motion detection purposes. The continuous steps presented in this paper are thresholding, noise removal and motion region estimation. Thresholding determines the areas of output images (from subtraction) consisting of pixels with values lying within the threshold value range. Threshold value also indicates the sensitivity of motion to detection. The image still contains a small amount of noise after thresholding. Noise is removed using a median filtering method. Motion region estimation is done by executing an AND or OR operation on two subtracted images from three successive frames. Both the output image from AND and OR operations will estimate the motion region in a different time frame.

6 citations


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