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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TL;DR: Wiener filtering techniques using a Markovian covariance model for the image signal are applied to the transformed data followed by an inverse transformation to restore the degraded image.
Abstract: A transformation to convert signal-dependent noise corrupting an image to additive Gaussian signal-independent noise is derived in this paper. Wiener filtering techniques using a Markovian covariance model for the image signal are applied to the transformed data followed by an inverse transformation to restore the degraded image. An ad hoc technique using contrast manipulation to adaptively convert signal-dependent noise to signal-independent noise is also described. The results of the computer simulations designed to evaluate the performance of these techniques are also presented.
23 citations
TL;DR: The experimental results for the proposed method demonstrate that it is faster and simpler than even median filtering, and it is very efficient for images corrupted with a wide range of impulse noise densities varying from 10% to 90%.
Abstract: This paper proposes a two-stage adaptive method for restoration of images corrupted with impulse noise. In the first stage, the pixels which are most likely contaminated by noise are detected based on their intensity values. In the second stage, an efficient average filtering algorithm is used to remove those noisy pixels from the image. Only pixels which are determined to be noisy in the first stage are processed in the second stage. The remaining pixels of the first stage are not processed further and are just copied to their corresponding locations in the restored image. The experimental results for the proposed method demonstrate that it is faster and simpler than even median filtering, and it is very efficient for images corrupted with a wide range of impulse noise densities varying from 10% to 90%. Because of its simplicity, high speed, and low computational complexity, the proposed method can be used in real-time digital image applications, e.g., in consumer electronic products such as digital televisions and cameras.
23 citations
TL;DR: Experimental results show that the proposed impulse noise removal scheme is capable of removing impulse noise effectively while preserving the fine image details and has shown effectiveness against high impulse noise density.
Abstract: Generally, the impulse noise filtering schemes use all pixels within a neighborhood and increase the size of neighborhood with the increase in noise density. However, the estimate from all pixels within neighborhood may not be accurate. Moreover, the larger window may remove edges and fine details as well. In contrast, we propose a novel impulse noise removal scheme that emphasizes on few noise-free pixels and small neighborhood. The proposed scheme searches noise-free pixels within a small neighborhood. If at least three pixels are not found, then the noisy pixel is left unchanged in current iteration. This iterative process continues until all noisy pixels are replaced with estimated values. In order to estimate the optimal value of the noisy pixel, genetic programming-based estimator is developed. The estimator (function) is composed of useful pixel information and arithmetic functions. Experimental results show that the proposed scheme is capable of removing impulse noise effectively while preserving the fine image details. Especially, our approach has shown effectiveness against high impulse noise density.
23 citations
09 Jul 2003
TL;DR: A method is proposed that leads to the automatic design of easily testable circuits and a class of image filters in which the evolutionary approach consistently produces excellent and innovative results, including "salt and pepper" noise filters and edge detectors.
Abstract: The paper deals with a class of image filters in which the evolutionary approach consistently produces excellent and innovative results. Furthermore, a method is proposed that leads to the automatic design of easily testable circuits. In particular we evolved "salt and pepper" noise filters, random shot noise filters, Gaussian noise filters, uniform random noise filters, and edge detectors.
23 citations
07 Jun 2004
TL;DR: New methods for estimating the distribution parameters of two of such sources of noise: dark current and the so-called fixed pattern noise are proposed, which require knowledge about the scene illumination.
Abstract: The irradiance measurement performed by vision cameras is not noise-free due to both processing errors during CCD fabrication and the behaviour of the electronic device itself. A proper characterization of sensor performance, however, allows either removing the resulting noise from the image or accounting for it within image processing algorithms. This paper proposes new methods for estimating the distribution parameters of two of such sources of noise: dark current and the so-called fixed pattern noise. Since both methods require knowledge about the scene illumination, an estimation method using a calibrating sphere is also presented. This method models illumination as the combination of directional and ambient lighting. Experimental results can be found at the end of the paper.
23 citations