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.

Image‐intensified photodiode array as a fluorescence detector in cw‐laser experiments

Abstract: Imaging systems based on image‐intensified photodiode‐array cameras are excellent detectors for laser‐induced fluorescence experiments in fluid mechanics and combustion science. The principles of operation of such a system are described. Special attention is given to the use of an image intensifier in conjunction with cw‐laser experiments. In that mode, ghost images caused by the finite phosphor decay time can contribute major systematic errors. Measurements of the phosphor decay times for exposure times between 0.1 and 100 ms (a typical range for cw‐laser experiments) were conducted and show that the decay time increases with exposure time. Methods for circumventing the ghosting problem are suggested. The signal and noise analysis points to analog‐to‐digital converter noise (ADC) or quantization error of the camera and to photon shot noise as the dominating noise sources. The image intensifier improves time resolution and signal‐to‐noise ratio (SNR) by moving the system from the camera noise limit to the shot‐noise limit. Once the shot‐noise limit is reached, the SNR can only be improved by increasing the quantum efficiency of the intensifier, not by increasing the intensifier gain. The spatial resolution of such a system is generally limited by the photodiode array, but can be dominated by focusing errors, if lenses with low f numbers are used. Within a certain range of imaging magnifications, the use of a fiber‐optic minifier between intensifier and array in lieu of a 1:1 fiber bundle can improve the collection efficiency, and thereby both signal and camera‐limited SNR. Use of a minifier will always improve the shot‐noise‐limited SNR.

Correlation test between digital camera parameters and visual noises

Abstract: Noise and unwanted signals as a response of an imaging process is an important factor in digital imaging systems. The standardization of noise measurement in determining the quality of digital imaging is in International Standard of Organization (ISO15739:2005). Since the amount of noise may vary significantly with exposure time and ISOs, the visibility of noise to human observers depends on the viewing distance, spatial frequency, density, color, and viewing condition. This concept is applied to this study, which is called dasiavisual noise,psila and it is measured by digital camera parameters such as ISOs and shutter speed variables applying the noise reduction option on and off. In this study, we will present the practical digital camera parameter for minimizing visual noise to consumers and expect efficiency of noise reduction from digital camera manufacturers. Furthermore, we can expect the following study to measure the human spatial response related to visual noise recognition.

Design of a new filtering for the noise removing in images by fuzzy logic

Abstract: Removing noise from the color images is a very active research scope in image processing. In this paper, a new fuzzy based image filtering algorithm is proposed for reducing and removing impulse noise in color images. For dealing with the Impulse noise, an algorithm is developed to search for a set of uncorrupted pixels in the neighborhood of the pixel of interest and to compute the median of this set. A modified fuzzy filter consisting of two sub filters with novel membership functions is proposed to cancel out the impulse noise. The first sub filter detects the noisy pixel by utilizing three fuzzy membership functions, defined for this purpose. The corrupted pixels are then corrected using the median of the noise free pixels. The second sub filter makes use of the relation between different color components of a pixel to remove the residual noise in the color image. Simulation results shows that the proposed fuzzy filter effectively removes the additive noise by preserving fine details in the image.

Removal of Salt and Pepper Noise Using Sparse Representation

Abstract: This paper proposes a new method to remove salt and pepper noise in gray scale images as well as color images. This is done in two stages. In the first stage impulse noise detection scheme is employed to detect the noisy pixel. This filter uses the morphological noise detector to classify the pixels as either corrupted or uncorrupted. In the second stage we treat the noisy pixel as a missing pixel in the image and apply in painting based on the sparse representations of the pixels that are identified as corrupted pixels in the first stage. Our experimental results show that the proposed method outperforms other best known denoising techniques used for the removal of salt and pepper noise, both visually and quantitatively. Extensive experiments have been carried out on different images to validate the efficiency of the proposed method.

Image Denoising Using Multiresolution Analysis and Nonlinear Filtering

Abstract: In multiresolution analysis, image is analyzed at different resolutions of pixels. The features in an image appear at different resolutions in different forms. Hence separation of features in the image is possible. In denoising problems, signal and noise can be separated in the process and hence elimination of noise becomes easier. It is proposed in this paper that when a nonlinear median filter is used in multiresolution environment, once in full resolution and then with half resolution, denoising becomes more perfect. This technique is a non linear processing and is found to be useful in reducing not only impulse noise but also Gaussian and Speckle noise. Further, it is also proposed that use of a nonlinear adaptive median filter produces more pleasing image with better denoising. It is also shown that the proposed method is useful for color image denoising too.