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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TL;DR: This research proposes a method for improving image quality by applying dither signal injection, which involves minimizing the noise that occurs in scan control circuits, which injects both noise and electron beams emitted in the course of A/D (analog to digital).
3 citations
25 Mar 2012
TL;DR: This article presents image processing methods for biological Images which show kind of cells, where Neighborhood algorithm, such as the Wiener filter was used for noise removal and application of morphological operations for suppressing the remaining effects of the noise on the original image.
Abstract: This article presents image processing methods for biological Images which show kind of cells. The main difficulty of processing these images was the presence of noise in the images and the limitation of losing pixels during the binarization of the images. Neighborhood algorithm, such as the Wiener filter was used for noise removal. In addition to this, a normalization of the background was performed on the image before the binarization of the image and finally the application of morphological operations for suppressing the remaining effects of the noise on the original image.
3 citations
10 Nov 2014
TL;DR: An efficient algorithm has been developed which is based on the study of singular values of noise corrupted images and estimates the noise level in images that is further used for setting up a threshold for wavelet denoising, which results in a better quality of denoised image.
Abstract: The paper is based on channel noise estimation and its reduction in compressed images using singular value decomposition. Image compression reduces irrelevant and re- dundant image data so that image is stored in lesser space and can be transmitted efficiently. Reducing the storage area increases the capacity of storage medium as well as the channel bandwidth. However, when the compressed image is transmitted, noise is added to it via transmission channel and the image is distorted. Therefore, accurate noise estimation and its reduction in a wide variety of vision and image processing applications is an important issue. An efficient algorithm has been developed which is based on the study of singular values of noise corrupted images and estimates the noise level in images that is further used for setting up a threshold for wavelet denoising. This dependency of threshold value on the esimation of noise level results in a better quality of denoised image. This algorithm has been applied to JPEG and JPEG2000 compressed images and corresponding results have been analyzed in terms of parameters like MSE and PSNR. The algorithm is more reliable, shows robust behavior over a visual content and noise conditions and it is more efficient as compared to the other relevant existing methods.
3 citations
TL;DR: The algorithms for the thermally generated charge elimination using the Discrete Wavelet Transform (DWT) and Bayesian estimators were proposed, and it is good to note that uncorrected images are not suitable for subsequent investigation.
Abstract: It is generally known that every astronomical image, which was acquired by CCD sensor, has to be corrected by dark frame. The Dark frame maps the thermally generated charge of the CCD. May become that the dark frame image is not available and it is impossible to correct the astronomical images directly. It is good to note that uncorrected images are not suitable for subsequent investigation. There are simple nonlinear filtering methods, e.g. median filtering, but the obtained results are not so satisfactory. During the recent year the algorithms for the thermally generated charge elimination were proposed. All these algorithms use the Discrete Wavelet Transform (DWT). The DWT transforms image into different frequency bands. Wavelet coefficients histogram should be modeled by generalized Laplacian probability density function (PDF). The Laplacian parameters were estimated by moment method using derived equation system. Furthermore, the images, where the thermally generated charge was suppressed, were estimated using Bayesian estimators. This algorithm will be in the future improved, but now to the promising eliminating algorithm should be involved.
3 citations
TL;DR: A method that can remove view-disturbing noise from image sequences by spatio-temporal image processing is developed that uses image sequences captured with a pan-tilt camera to extract adherent noise regions.
Abstract: We have developed a method that can remove view-disturbing noise from image sequences by spatio-temporal image processing. In outdoor environments on rainy days, pictures taken by a camera are often degraded because of adherent noise, such as water drops on the surface of the lens-protecting glass of the camera. To solve this problem, our method uses image sequences captured with a pan-tilt camera. The method uses a spatio-temporal image to extract adherent noise regions by examining trajectory differences between adherent noise and other objects in cross-section images. Finally, adherent noise regions are filled in with surrounding information in the cross-section image. Experimental results showed our method is effective.
3 citations