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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
01 Aug 2013
TL;DR: Through the comparative analysis of the signal to noise ratio, it is proved that this method can get better de-noising effect and the marine radar image is gotten after de- noising.
Abstract: Marine radar image often has a lot of noise signal, the noise ratio of the radar image is very low. So a method based on a combination of wavelet transform, K-distributed sea clutter threshold segmentation and most of the interpolation repair method is presented in this paper, which can deal with the same frequency noise of marine radar image. This paper comes up with use wavelet transform to determine potential radar image noise level, then by adopting the method of threshold segmentation technique for marine radar information on the potential position detection of same frequency noise, to repair most of the interpolation method is used to deal with noise in the radar image. Finally, the marine radar image is gotten after de-noising. Through the comparative analysis of the signal to noise ratio, it is proved that this method can get better de-noising effect.

3 citations

Proceedings ArticleDOI
29 Dec 1993
TL;DR: A simple and cheap method for measuring the response of the imaging system to low modulation white noise is presented, from obtaining the test targets to producing the M.T.F. graphs.
Abstract: The M.T.F. of an image capture system is a good measure of a system's ability to reproduce sharp pictures. Post image capture processing will enhance the sharpness of the picture but the amount and type of processing required to produce a sharp picture is affected strongly the the M.T.F. of the image capture system. A simple and cheap method for measuring the M.T.F. in these circumstances is to measure the response of the imaging system to low modulation white noise. One such source of low modulation white noise is grain noise present in photographic transparencies. If the grain noise is captured with the image capture system and then Fourier transformed a M.T.F. response is produced. Presented is a practical example of this technique, from obtaining the test targets to producing the M.T.F. graphs. Also presented are some of the pitfalls that can be encountered using the technique.

3 citations

Journal ArticleDOI
TL;DR: In this paper, a modified version of the well-known spectral subtraction is used for noise cancellation in a speech-passing angle grinder noise-canceling headset, which is adapted very quickly to the nonstationary noise environment while inflecting minimum musical noise and speech distortion on the processed signal.
Abstract: This paper deals with configuration of an algorithm to be used in a speech-passing angle grinder noise-canceling headset. Angle grinder noise is annoying and interrupts ordinary oral communication. Meaning that, low SNR noisy condition is ahead. Since variation in angle grinder working condition changes noise statistics, the noise will be nonstationary with possible jumps in its power. Studies are conducted for picking an appropriate algorithm. A modified version of the well-known spectral subtraction shows superior performance against alternate methods. Noise estimation is calculated through a multi-band fast adapting scheme. The algorithm is adapted very quickly to the non-stationary noise environment while inflecting minimum musical noise and speech distortion on the processed signal. Objective and subjective measures illustrating the performance of the proposed method are introduced.

3 citations

Proceedings ArticleDOI
01 Mar 2016
TL;DR: In the field of switching filter, a highly effective filter to restore extremely corrupted image with impulse noise is presented and it performs better than other approaches to impulse noise removal, in terms of suppressing impulse noise while preserving image details.
Abstract: In the field of switching filter, a highly effective filter to restore extremely corrupted image with impulse noise is presented. It is capable of handling low density as well as high density of random valued and fixed valued impulse noise. In this study, local area comprises within the window in an image is analyzed for intensity extrema to classify the pixel as either noisy or noiseless. Filtering is applied to the noisy pixels only and it is done in such a way that the noisy pixel is replaced by either the median or the mean value of the filtering window depending on the noiseless pixels present in the window. The window size is adaptive for this filter and depends on the estimated noise density. The proposed filter is tested on a large number of grayscale and color images under a wide range of noise density (from 10% to 94%) and the simulation results reveal that it performs better than other approaches to impulse noise removal, in terms of suppressing impulse noise while preserving image details. The proposed filter is simple to implement and suitable for real time implementation.

3 citations

Proceedings ArticleDOI
01 Oct 2016
TL;DR: This paper presents a two-step restoration algorithm for impulse noise detection and removal and shows that compared with the other filters, it can provide better performances in both quantitatively and visually.
Abstract: This paper presents a two-step restoration algorithm for impulse noise detection and removal. In the detection step, the pixel which is most likely corrupted by noise is detected according to its gray values. In the removal step, the proposed algorithm adaptively alters the filtering window size depending on the noise density. For a noisy pixel, if there exist one or more noise-free pixels in its window, the spatial correlation-based weighted mean filter will be applied to it by using only noise-free pixels. Otherwise, we use the median filter to correct the detection errors and remove noise. Naturally, the noise-free pixels are retained. Experimental results show that compared with the other filters, our algorithm can provide better performances in both quantitatively and visually.

3 citations


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