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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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Journal ArticleDOI
TL;DR: The computed value is used to reduce Gaussian noise and eliminate defective pixels in a raw digital image and is particularly suitable for implementation in low power mobile devices with imaging capabilities such as camera phones, as well as digital still cameras (DSC).
Abstract: This paper describes a fast method for noise level estimation and denoising. Specifically, we address the problem of estimating the standard deviation of additive white Gaussian noise in digital images; the computed value is used to reduce Gaussian noise and eliminate defective pixels in a raw digital image. The method is particularly suitable for implementation in low power mobile devices with imaging capabilities such as camera phones, as well as digital still cameras (DSC).

39 citations

Patent
11 Dec 2000
TL;DR: In this article, a method is described for enhancing a digital image channel by providing a predetermined estimate of the noise expected for the digital channel based on a predetermined relationship between the image intensity values and the expected noise for given intensities.
Abstract: A method is described for enhancing a digital image channel, e.g., a channel comprising a texture signal, by providing a predetermined estimate of the noise expected for the digital channel based on a predetermined relationship between the image intensity values and the expected noise for given intensities. After a local estimate of signal activity is generated for the digital image channel, a gain adjustment is generated from the predetermined estimate of noise and the local estimate of signal activity; the gain adjustment is applied to the image pixels in the digital channel in order to generate a digital channel with enhanced image values.

38 citations

Proceedings ArticleDOI
23 Aug 2004
TL;DR: An algorithm that discriminates moving objects from their shadows is presented using the mean shift algorithm, which is very powerful in non-parametric clustering of data.
Abstract: An algorithm that discriminates moving objects from their shadows is presented. Starting from the change mask of an image sequence, first of all the changed area is divided into subregions consisting of pixels with similar colour properties. This is done using the mean shift algorithm, which is very powerful in non-parametric clustering of data. In a second step a significance test is performed to classify each image pixel inside the change mask into one of the classes foreground or shadow. To do this a straightforward image model is used where the grey level of a foreground pixel covered by a shadow is given by the product of the corresponding background pixels' grey-level and a constant value. Assuming that fore- and background images are corrupted by Gaussian white noise, a significance test is derived which classifies all pixels inside the change mask. In the third step global and local information from the first and second steps are combined. For each region inside the change mask it is examined if the majority of pixels survived the second step. If this is the case, the whole region is kept for the final moving object mask, if not the region is set to zero.

38 citations

Patent
20 Sep 2007
TL;DR: In this paper, a method and apparatus to remove color noise included in raw data while effectively preventing image quality degradation was proposed, where the pixel value for noise removal with noise removed is converted into the source pixel value, whereby only color noise can be removed without affecting a luminance signal.
Abstract: A method and apparatus to remove color noise included in raw data while effectively preventing image quality degradation. For Interest pixels serially set onto a mosaic image formed of raw data, conversion is executed into a pixel value for noise removal based on a processing reference pixel value having a unified color signal component in each interest pixel, noise is removed from the pixel value for noise removal, and the pixel value for noise removal with noise removed is converted into the source pixel value, whereby only color noise can be removed without affecting a luminance signal.

37 citations

Journal ArticleDOI
TL;DR: An integrating CMOS image sensor with a wide dynamic range is described in this paper, where the dynamic range of these pixels is controlled by a user-defined reference voltage that creates a photocurrent-dependent effective integration time.
Abstract: An integrating CMOS image sensor with a wide dynamic range is described. The dynamic range of these pixels is controlled by a user-defined reference voltage that creates a photocurrent-dependent effective integration time. The operation of these pixels and a method of obtaining a well-controlled logarithmic response are both described. Furthermore, described are the results of two alternative methods of correcting the fixed pattern noise in these pixels and measurements of the temporal noise from individual pixels. These results show that with these pixels, it is possible to match the contrast sensitivity of the human visual system.

37 citations


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