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.

A Radon-Transform-Based Image Noise Filter—With Applications to Multibeam Bathymetry

Abstract: This paper describes a linear-image-transform-based algorithm for reducing stripe noise, track line artifacts, and motion-induced errors in remote sensing data. Developed for multibeam bathymetry (MB), the method has also been used for removing scalloping in synthetic aperture radar images. The proposed image transform is the composition of an invertible edge detection operator and a fast discrete Radon transform (DRT) due to Gotz, Druckmuller, and Brady. The inverse DRT is computed by using an iterative method and exploiting an approximate inverse algorithm due to Press. The edge operator is implemented by circular convolution with a Laplacian point spread function modified to render the operator invertible. In the transformed image, linear discontinuities appear as high-intensity spots, which may be reset to zero. In MB data, a second noise signature is linked to motion-induced errors. A Chebyshev approximation of the original image is subtracted before applying the transform, and added back to the denoised image; this is necessary to avoid boundary effects. It is possible to process data faster and suppress motion-induced noise further by filtering images in nonoverlapping blocks using a matrix representation for the inverse DRT. Processed test images from several MB data sets had less noise and distortion compared with those obtained with standard low-pass filters. Denoising also improved the accuracy in statistical classification of geomorphological type by 10–28% for two sets of invariant terrain features.

Influence of illumination on dark current in charge-coupled device imagers

Abstract: Thermal excitation of electrons is a major source of noise in charge-coupled-device (CCD) imagers. Those electrons are gen- erated even in the absence of light, hence, the name dark current. Dark current is particularly important for long exposure times and elevated temperatures. The standard procedure to correct for dark current is to take several pictures under the same condition as the real image, except with the shutter closed. The resulting dark frame is later subtracted from the exposed image. We address the ques- tion of whether the dark current produced in an image taken with a closed shutter is identical to the dark current produced in an expo- sure in the presence of light. In our investigation, we illuminated two different CCD chips with different intensities of light and measured the dark current generation. A surprising result of this study is that some pixels produce a different amount of dark current under illumi- nation. Finally, we discuss the implication of this finding for dark frame image correction. © 2009 SPIE and IS&T.

Imaging system, image processing method, and image processing program

Abstract: A favorable noise reduction process that is optimized for capturing conditions and that prevents the occurrence of residual image components is enabled. Provided is an imaging system including: a first extraction section that extracts a local region that includes a pixel of interest from an image signal; a second extraction section that extracts, from another image signal captured at a different time, a local region located at almost the same position as said local region; a first noise reduction section that performs a noise reduction process by using the local regions; a noise estimation section that estimates an amount of noise included in the pixel of interest; a residual image detection section that detects a residual image component included in the local region based on the estimated amount of noise; and a second noise reduction section that performs a noise reduction process based on the detected residual image component.

Dark frame subtraction using multiple dark frames

Abstract: The specification and drawings present a new method, apparatus and software product for a dark frame subtraction using multiple dark frames by storing only one frame at a time, i.e., using only one frame storage so that the amount of memory can be minimized. Divisional and multiplication algorithms can be used for the dark frame subtraction.

Color edge detection using the Skellam distribution as a sensor noise model

Abstract: Conventional edge detectors suffer from inherent image noise and threshold determination. In this paper, we propose a noble edge detector based on the noise distribution for CCD or CMOS cameras. By assuming the dominant photon noise, we model the distribution of intensity differences between two neighborhood pixels. Since it is well known that photon noise follows a Poisson distribution, we introduce a Skellam distribution, which is the difference of two Poisson random variables. We show experimentally that the Skellam distribution can be used to model the noise distribution of pixels that are captured from the same scene radiance. For estimating the noise distribution given a single pixel, we find the important property that the Skellam parameters are linearly related to the intensity value of pixels. This linearity enables us to determine noise parameters according to the intensity value. In addition, parameters of the line are preserved under illumination, scene and camera setting changes except for only a gain change. Based on the noise distributions, we calculate intensity allowances of three channels for each pixel given a confidence interval. We propose a noble edge detector by skipping a pre-processing step of conventional Gaussian smoothing which is the main obstacle for robust and accurate edge detection. If the difference of intensity exceeds the intensity allowance at least in a single channel, the in- between pixel is marked as an edge pixel. We demonstrate that without conventional Gaussian smoothing the noise-model based approach can automatically extract the fine details of image structures, such as edge and corners, independent of camera setting.