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.

Application of SUSAN to reduce the speckle noise of remote sensing image

Abstract: Speckle noise can be introduced to a remote sensing image in many ways, starting with the lens of the imaging hardware and ending at the digitization of the captured image. The reduction of noise without degradation of the remote sensing image has attracted much attention in the past. However, the traditional noise reduction methods can usually cause the degradation of the underlying image and cannot preserve the feature of structure in remote sensing image, especially two dimensional image brightness structures. With regard to the traditional speckle noise reduction methods, their results aren't very well even though the traditional methods are improved. In this paper, a method for speckle noise reduction of remote sensing image based on SUSAN is designed. This paper tests this method in a SPOT image of 128*128 suffering from speckle noise using 3 by 3 and 5 by 5 mask and gives results of quantitative and qualitative comparisons of the SUSAN noise filter with other traditional noise reduction methods. The results of the test prove that the SUSAN filter can effectively remove speckle noise and preserve edge and texture information. The processing speed of this algorithm is faster than that of the traditional noise reduction methods.

An intelligent hardware structure for impulse noise suppression

Abstract: In this paper an intelligent hardware module suitable for the computation of an adaptive median filter (AMF) is presented. The proposed digital hardware structure is pipelined and parallel processing is used to minimize computational time. It is capable of processing gray-scale images of 8-bit resolution with 3/spl times/3 or 5/spl times/5-pixel image neighborhoods as options for the computation of the filter output. However, the system can be easily expanded to accommodate windows of larger sizes. The function of the proposed circuitry is to detect the existence of impulse noise in an image neighborhood and apply the median filter operator only when necessary. Moreover, the noise detection procedure can be customized so that a range of pixel values is considered as impulse noise. In this way, the integrity of edge and detail information of the image under process is preserved and blurring is avoided. The proposed digital structure was implemented in FPGA and it can be used in industrial imaging applications, where fast processing is of the utmost importance. As an example, the time required to perform filtering of a grayscale image of 260/spl times/244 pixels is approximately 7.6 msec. The typical system clock frequency is 65 MHz.

Motion and Noise Detection for Adaptive Spatio-Temporal Filtering of Medical X-Ray Image Sequences

Abstract: Spatio-temporalfilters are used to improve the perceived quality of X-ray image sequences exhibiting severe noise in real-time. The strength of spatial and temporal filtering has to be adapted locally in order to avoid artifacts. We propose a method processing the positive and negative pixel values of difference images independently in order to detect regions dominated by motion and single pixels dominated by noise. In the context of threshold determination for noise-adaptive binarization using Euler numbers, the influence of noise and motion on Euler curves is investigated.

Noise-optimal capture for coded exposure photography

Abstract: Searching for the optimal shutter sequence is the key problem in coded exposure photography. Previous shutter sequence searching methods focus on the point spread function estimation and invertibility, and ignore the influence of the scene light level or avoid noise calibration of real cameras. For practical purposes, we address the problem of finding an optimal shutter sequence for coded exposure photography in the presence of photon noise. We analyze the effect of photon noise on the optimal shutter sequence in terms of deconvolution noise and derive analytic formulas. We show that Raskar's code is a special case of our analysis. Based on noise calibration of the coded exposure camera, an effective fitness function is proposed, and using our carefully designed genetic algorithm, we obtain the optimal shutter sequence with little running time. Experimental results with synthetic and real data demonstrate the advantage of our approach compared to the state of the art approach.