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.
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20 May 2005TL;DR: In this paper, the authors proposed a noise reduction device consisting of an image storage unit, a blackout image processing unit, and a noise processing unit to reduce a noise in the image data based on specific noise component of the blackout image data.
Abstract: The noise reduction device includes an image storage unit, a blackout image processing unit, and a noise processing unit. The image storage unit captures image data obtained by imaging a field with an image sensor, and stores the image data therein. The blackout image processing unit captures blackout image data obtained by imaging by the image sensor that is shaded, and extracts a specific noise component of the blackout image data. The noise processing unit reduces a noise in the image data based on the specific noise component of the blackout image data.
10 citations
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TL;DR: It was found by extrapolation to clinical demagnifications that the amplifier noise dominates x-ray quantum noise, at all spatial frequencies, but the shot noise was less than the x-rays quantum noise at low spatial frequencies; this implies that a secondary quantum sink can be avoided.
Abstract: In fluoroscopic portal imaging systems, a metal plate is bonded to a phosphor screen and together these act as the primary x‐ray sensor. The light from the screen is collected and imaged by a lens on the target of a video camera. The demagnification (M) between the large area of the phosphor being imaged and the small active area of the video camera results in poor optical coupling between the screen and the video camera. Consequently x‐ray quantum noise is small compared to other noise sources. By reducing the demagnification, the light from the screen is collected more efficiently, so we were able to increase the x‐ray quantum noise relative to other noise sources and thus unambiguously identify it. The noise power spectrum was measured as a function of M to determine the relationship between the x‐ray quantum noise, shot noise, and amplifiernoise. It was found by extrapolation to clinical demagnifications that the amplifiernoise dominates x‐ray quantum noise at all spatial frequencies, but the shot noise was less than the x‐ray quantum noise at low spatial frequencies. For low spatial frequencies, this implies that a secondary quantum sink can be avoided. If amplifiernoise could be sufficiently reduced, x‐ray quantum limited images could be obtained in clinical systems at low spatial frequencies.
10 citations
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26 May 2013TL;DR: This work creates noise image data set by taking photos of random noises displayed on a high definition monitor and proposes a homomorphic based SPN extraction method, which only needs to denoise once, which is highly efficient to deal with large numbers of photos.
Abstract: The sensor pattern noise (SPN) can be regarded as the unique identity of a digital camera which is highly useful in digital image forensics [1, 2]. Existing methods [1, 2] which works by denoising each individual natural image often took an investigator a long time and great efforts to collect sufficient photos of diversified enough natural scenes. These processes are hard to repeat or standardized for officially using by an authority. In this work, we create noise image data set by taking photos of random noises displayed on a high definition monitor and propose a homomorphic based SPN extraction method. It offers the forensic researcher a fast way to create a large image data set in a few minutes. And the extraction method only needs to denoise once, which is highly efficient to deal with large numbers of photos. We compared the source camera identification performance of the proposed SPN extraction method to a prior state-of-art with identical experimental settings. The experimental results confirm the effectiveness of the proposed method.
10 citations
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12 Jan 2001TL;DR: In this article, the authors proposed a method and apparatus for dithering for color computer display systems that includes the addition of a noise component to each of the color components of each pixel in a pseudo-random manner.
Abstract: A method and apparatus for dithering for color computer display systems includes the addition of a noise component to each of the color components of each pixel in a pseudo-random manner. The noise component is preferably different for each color component. Taking the image as a whole, the noise component repeats on a regular basis but is preferably selected so as not to repeat on adjacent pixels. The image is divided into squares of pixels and the same noise component is added to each of the same relative pixels from square to square. The preferred square of pixels is four pixels wide by four pixels high. The value of the noise component is chosen such that the most significant bit alternates both horizontally and vertically from pixel to pixel within the square of pixels. The other bits of the value of the noise component are preferably chosen such that the value of the noise component does not repeat within the square of pixels and such that a simplified hardware implementation is made possible by their selection. The resulting hardware implementation preferably consists of a number of exclusive-or gates tied together to produce the value of the noise component based on the least significant bits of the X and Y coordinates of each pixel. This hardware implementation is simple enough that it becomes economically practical to add a different noise component to each of the three color components of each pixel rather than the same noise component to all of the color components.
10 citations
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12 Nov 2007TL;DR: Noise and signal activity estimation method that discriminates noise from signal based on local and global properties of the image data, which yields pixel-wise maps of the noise variance and of the signal activity.
Abstract: In this work, we propose noise and signal activity estimation method that discriminates noise from signal based on local and global properties of the image data. The method yields pixel-wise maps of the noise variance and of the signal activity. Using these maps to guide imaging algorithms such as image enhancement and print defect detection improves their performance. The proposed method does not assume a white Gaussian noise model; it is very efficient computationally and, as such, is useful for a wide variety of applications.
10 citations