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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.


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Journal Article
TL;DR: In order to reduce the impact on the errors, the image noise of the image sensor is analyzed, and an experiential image noise model have been set up, the experimental results prove this model is effectual and applied.
Abstract: For the non-linearity of the reflectance of laser stripe on the work piece and performance of camera lens, the random noise added in the process of photoelectricity conversion, the storage, transmission and export of the electric charge of signal having the noises such as dark current, the video signal produced noises when transmitted, quantification noises hade by the A/D conversion of the video signal,definite error of the digital image signal will be resulted in the processes of acquiring the weld's information of the computer vision system of welding robot. In order to reduce the impact on the errors, the image noise of the image sensor is analyzed,and an experiential image noise model have been set up, the experimental results prove this model is effectual and applied.

2 citations

Journal ArticleDOI
TL;DR: Both quantitative and qualitative analyses show the superiority of the proposed filter over other existing filters, and the proposed method is applied to both colour images and greyscale images, and better results are obtained.
Abstract: In this paper, a neural network-based Adaptive-Size Median (ASMED) filter is proposed for impulse noise removal. This method consists of two stages: noise detection and noise filtering. Noise detection is performed by a neural network-based detector, and filtering is applied only to corrupt pixels in the noisy image. Extensive experimental analysis shows that the proposed technique can be used for images with different impulse noise models. Both quantitative and qualitative analyses show the superiority of the proposed filter over other existing filters. The proposed method is applied to both colour images and greyscale images, and better results are obtained.

2 citations

Journal Article
TL;DR: Experiments prove that the presented noise suppression method can reduce noise, increase signal to noise ratio and improve image quality.
Abstract: Aim\ In order to reduce the noise of scientific grade CCD camera and improve the image quality.Methods\ The noise characteristic of CCD image sensor analyzed. Noise suppression method and signal processing circuit are described and designed analyzed. Results\ The circuit can reduce the dark current noise and eliminate the reset noise effectively.Conclusion\ Experiments prove that the presented method can reduce noise, increase signal to noise ratio and improve image quality.

2 citations

Proceedings ArticleDOI
09 Nov 1997
TL;DR: A method of automatically estimating noise in 2and 3D emission images from the information intrinsic to them and then using this estimate to selectively remove the noise from the image to improve image uniformity without degrading resolution or contrast.
Abstract: The authors have developed a method of automatically estimating noise in 2and 3D emission images from the information intrinsic to them and then using this estimate to selectively remove the noise from the image. Unlike low cutoff filters, which reduce resolution and contrast, the morphing operation removes noise without adversely affecting these qualities. The noise estimation method uses contiguous volume analysis to obtain information about all of the hot spots in the image. If the image contains hot regions too small to contain information, the method analyzes their sizes and heights. This information is used to find a conservative estimate of noise for the image. The information about noise is used to perform an image morphing using a noise-specific structure. Erosion and dilation are morphing operations which remove image detail smaller than the specified structure, without distortion of the larger features. When applied to PET and SPECT images using the correct structure, these operations remove only noise, thereby improving image uniformity without degrading resolution or contrast.

2 citations

Book ChapterDOI
29 Sep 2004
TL;DR: By successfully identifying the corrupted and uncorrupted pixels, especially for the pixels nearing the edges of a given image, one can eliminate random-valued impulse noise while preserving the detail of the image and its information of the edges.
Abstract: It deals an algorithm for removing the impulse noise, which is also called salt-pepper noise, in this paper. By evaluating the absolute differences of intensity between each point and its neighbors, one can detect the edges, the isolated noise points and blocks. It needs to set up a set of simple rules to determine the corrupted pixels in a corrupted image. By successfully identifying the corrupted and uncorrupted pixels, especially for the pixels nearing the edges of a given image, one can eliminate random-valued impulse noise while preserving the detail of the image and its information of the edges. It shows, in the testing experiments, that it has a better performance for the algorithm than the other’s mentioned in the literatures.

2 citations


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