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.

Noise modeling and estimation in image sequences from thermal infrared cameras

Abstract: In this paper we present an automated procedure devised to measure noise variance and correlation from a sequence, either temporal or spectral, of digitized images acquired by an incoherent imaging detector. The fundamental assumption is that the noise is signal-independent and stationary in each frame, but may be non-stationary across the sequence of frames. The idea is to detect areas within bivariate scatterplots of local statistics, corresponding to statistically homogeneous pixels. After that, the noise PDF, modeled as a parametric generalized Gaussian function, is estimated from homogeneous pixels. Results obtained applying the noise model to images taken by an IR camera operated in different environmental conditions are presented and discussed. They demonstrate that the noise is heavy-tailed (tails longer than those of a Gaussian PDF) and spatially autocorrelated. Temporal correlation has been investigated as well and found to depend on the frame rate and, by a small extent, on the wavelength of the thermal radiation.

Noise reduction for image sequences

Abstract: Described herein are techniques related to noise reduction for image sequences or videos. This Abstract is submitted with the understanding that it will not be used to interpret or limit the scope and meaning of the claims. A noise reduction tool includes a motion estimator configured to estimated motion in the video, a noise spectrum estimator configured to estimate noise in the video, a shot detector configured to trigger the noise estimation process, a noise spectrum validator configured to validate the estimated noise spectrum, and a noise reducer to reduce noise in the video using the estimated noise spectrum.

An Image Sharpening Method by Suppressing the Noise

Abstract: Image Processing refers to the use of algorithm to perform processing on digital image. Microscopic images like some microorganism images contain different type of noises which reduce the quality of the images. Removing noise is a difficult task. Noise removal is an issue of image processing. Images containing noise degrade the quality of the images. Removing noise is an important processing task. After removing noise from the images, the visual effect will not be proper. Image Sharpening in an image is basically a process of extracting high frequency details from the image and then adding this information to the blurred image. This paper presents an approach to a de-noise method for noisy image and a method of sharpening of the noisy image.

Random valued impulse denoising using robust direction based detector

Abstract: Impulse noise is a spark that affects the contents of digital images. It also occurs during image acquisition, transmission due to malfunctioning of pixel elements in the camera sensors, faulty memory locations, timing errors in analog to digital conversion and bit errors during transmission. Removal of impulse noise involves detection followed by filtering mechanism. There are two types of impulse noise namely Salt and pepper impulse noise and Random valued impulse noise. Among them, random valued noise is difficult to detect. Among existing detection methods, most of them provide good results for low noise density images. Few methods deals with high density noise and provide less visual quality. In this paper, we propose a new detector; Robust Direction based Detector (RDD) which is based on ROR (Robust Outlyingness Ratio) and Standard Deviation (SD). Based on the ROR, the pixels are classified into noisy and noise free pixels. Then, different decision rules are adopted on the corrupted pixels again based on standard deviation. Then the corrupted pixels are filtered by using Edge Strength Interpolation Algorithm.

Noise Estimation and Reduction in Microarray Images

Abstract: Lots of error sources affect the microarray image quality, especially the noise. An image may contain different type noises which will produce distinct influence on image processing, so it doesn’t need to remove all. This paper analyzed the affection of different noises to automatic gridding and proposed grid line number for quantitive evaluation. A new algorithm for noise reduction was developed, which included two parts: edge noise reduction and highly fluorescence noise reduction. Edge detection was executed on the vertical and horizontal projections of microarray image. Highly fluorescent noise was removed by linear replace, which is an easy and fast means. The algorithm was implemented and compared to other common noise reduction methods. Experiment results show the feasibility of the proposed approach.