Topic
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 published on a yearly basis
Papers
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15 Jul 2004
TL;DR: The use of edge pattern analysis is examined both for automatic assessment of spatially variable noise and as a foundation for new noise reduction methods.
Abstract: Noise is the primary visibility limit in the process of non-linear image enhancement, and is no longer a statistically stable additive noise in the post-enhancement image. Therefore novel approaches are needed to both assess and reduce spatially variable noise at this stage in overall image processing. Here we will examine the use of edge pattern analysis both for automatic assessment of spatially variable noise and as a foundation for new noise reduction methods.
17 citations
TL;DR: The brightness levels in a scene or image, together with the spatial relationships among these levels, comprise the total input and output data available to the digital image processor.
Abstract: The brightness levels in a scene or image, together with the spatial relationships among these levels, comprise the total input and output data available to the digital image processor. Indeed, in any mathematical image processing operation, these are the elements of the image which are being manipulated and which represent the major concern of the image processing professional. However, many other factors affect image brightness relations and can distort or obscure the outcome of any image processing experiment. These factors, which consist of a long chain of transmitters, transducers, signal conditioners and processors, are in aggregate commonly called the image chain. An understanding of the image chain is essential to the design of image processing systems.
17 citations
TL;DR: An original method to estimate the noise introduced by optical imaging systems, such as CCD cameras, which relies on the multivariate regression of sample mean and variance.
Abstract: This article deals with an original method to estimate the noise introduced by optical imaging systems, such as CCD cameras. The power of the signal-dependent photon noise is decoupled from the power of the signal-independent electronic noise. The method relies on the multivariate regression of sample mean and variance. Statistically similar image pixels, not necessarily connected, produce scatterpoints that are clustered along a straight line, whose slope and intercept measure the signal-dependent and signal-independent components of the noise power, respectively. Experimental results carried out on a simulated noisy image and on true data from a commercial CCD camera highlight the accuracy of the proposed method and its applicability to separate R–G–B components that have been corrected for the nonlinear effects of the camera response function, but not yet interpolated to the the full size of the mosaiced R–G–B image.
16 citations
09 Jun 2017
TL;DR: The median filter is applied in order to reduce the amount of impulsive noise in a corrupted image and the approach that is called adaptive FT-based image filter (AFT-IF) is compared with the ones obtained by a number of other image filters forImpulse noise reduction.
Abstract: Impulse noise, also known as impulsive noise, is one of the most common types of noise occurring in digital images. The median filter and morphological filters are often used to remove impulsive noise, but these filters do not preserve image details. In this paper, we first apply the median filter in order to reduce the amount of impulsive noise in a corrupted image. After an application of the direct fuzzy transform (FT) to the resulting image, we restored the pixel values corresponding to locations flagged by the fuzzy rule-based noise detector by means of the inverse fuzzy transform. Finally, we obtained the output of our proposed image filter by combining the restored pixels with the ones marked as noiseless by the aforementioned noise fuzzy detector. We compared the results of our approach that we called adaptive FT-based image filter (AFT-IF) with the ones obtained by a number of other image filters for impulsive noise reduction.
16 citations
01 Jan 2014
TL;DR: Three different filtering algorithms such as Median filter, Weiner filter (WF) and Center Weighted Median filter (CWM) are used to remove the noise present in the MRI images and shows that the Weiner filters reconstructs a high quality image than Median and Center weighted Median filter.
Abstract: In Image Processing, the digital images are much sensitive to noise which results due to the Image Acquisition errors and transmission errors.MRI images captured usually are prone to speckle noise, Gaussian noise, salt and pepper noise etc., Image filtering algorithms are applied over the noisy images to remove the noise and preserve the image details. In this work three different filtering algorithms such as Median filter (MF), Weiner filter (WF) and Center Weighted Median filter (CWM) are used to remove the noise present in the MRI images. The performance of these filters are compared using the statistical parameters such as Mean Square Error(MSE) and Peak Signal to Noise Ratio(PSNR).The study shows that the Weiner filters reconstructs a high quality image than Median and Center weighted Median filter.
16 citations