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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.


Papers
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Proceedings ArticleDOI
16 Mar 2008
TL;DR: This research used Spectral Subtraction as a method to remove noise from speech signals using the fast Fourier transform and had recourse to the speech to noise ratio (SNR) in order to evaluate the performance of the proposed algorithm.
Abstract: We used Spectral Subtraction in this research as a method to remove noise from speech signals. Initially, the spectrum of the noisy speech is computed using the fast Fourier transform (FFT), then the average magnitude of the noise spectrum is subtracted from the noisy speech spectrum. We applied Spectral Subtraction to the speech signal "Hot dog" to which we digitally added vacuum cleaner noise. We implemented the noise removal algorithm by storing the noisy speech data into Hanning time-widowed half-overlapped data buffers, computing the corresponding spectrums using the FFT, removing the noise from the noisy speech, and reconstructing the speech back into the time domain using the inverse fast Fourier transform (IFFT). We had recourse to the speech to noise ratio (SNR )in order to evaluate the performance of the proposed algorithm.

16 citations

Patent
Ronald K. Minemier1
30 Jun 1999
TL;DR: In this article, the temperature of a silicon diode embedded on the same integrated circuit with the image sensor is used together with initial dark current calibration information, to provide dark current compensation on the fly during image capture in order to avoid the need for multiple shutter operations or repeatedly capturing a dark frame and then capturing a regular image frame.
Abstract: Dark current noise may be compensated for in a digital imaging sensor by measuring the temperature of a silicon diode embedded on the same integrated circuit with the image sensor This information may be used together with initial dark current calibration information, to provide dark current compensation on the fly during image capture In some embodiments this may avoid the need for multiple shutter operations or repeatedly capturing a dark frame and then capturing a regular image frame

16 citations

Patent
Masafumi Kamei1
30 Jun 1999
TL;DR: In this article, the correction data stored in a correction data storage unit is subtracted from video data read by a normal technique while the light source is ON in a corrections memory, thus executing correction for removing beat noise.
Abstract: Video data (default data in case of a black original) output from a CCD line sensor 405 upon reading an image while a light source is kept OFF corresponds to beat noise contained in video data obtained upon reading an image while the light source is ON. After the beat noise data is stored, the correction data stored in a correction data storage unit is subtracted from video data read by a normal technique while the light source is ON in a correction memory, thus executing correction for removing beat noise. After the beat noise is removed in this way, when an image is formed under the control of a printer control unit, an image free from any beat noise can be obtained as an output image.

16 citations

Proceedings ArticleDOI
23 Jul 2007
TL;DR: Experimental results show that the proposed operator exhibits superior performance over the competing operators and is capable of efficiently suppressing the noise in the image while at the same time effectively preserving the useful information in theimage.
Abstract: A novel filtering operator based on type-2 fuzzy logic techniques is proposed for detail preserving restoration of impulse noise corrupted images. The performance of the proposed operator is tested for different test images corrupted at various noise densities and also compared with representative conventional as well as state-of-the-art impulse noise removal operators from the literature. Experimental results show that the proposed operator exhibits superior performance over the competing operators and is capable of efficiently suppressing the noise in the image while at the same time effectively preserving the useful information in the image.

16 citations

Proceedings ArticleDOI
29 Jul 1993
TL;DR: Two locally adaptive image smoothing filters to improve the signal to noise ratio of digitized mammogram images are developed and implemented and the Bayesian estimator was found to outperform the adaptive Wiener filter.
Abstract: We developed and implemented two locally adaptive image smoothing filters to improve the signal to noise ratio of digitized mammogram images. The application of these smoothing filters in conjunction with the deconvolution of the images results in better visualization of image details. Previous efforts in restoration of digitized mammograms have assumed a stationary image with uncorrelated white Gaussian noise. In this work we considered a more realistic case of a non-stationary image model and signal-dependent noise of photonic and film-grain origins. Both the camera blur and the MTF of the screen-film combination were considered. The camera noise may be minimized through averaging and background subtraction. The signal-dependent nature of the radiographic noise was modelled by a linear shift-invariant system and the relative strengths of various noise sources were compared. The deconvolution filter was designed to respond to the particular form of the noise in the system based on the Minimum Mean Squared Error (MMSE) criteria. Of the two smoothing filters the Bayesian estimator was found to outperform the adaptive Wiener filter. Filters were implemented in a real time processing environment using our mammographic image acquisition and analysis system.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

15 citations


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