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.

An Image Restoration using Nonlinear Filter in Mixed Noise Environment

Abstract: The digital images are being degraded by noise in the process of acquisition, storage and transmission, Gaussian or impulse noise is the representative noise. Meanwhile, the image has lots of tendency to be degraded by complex noise, so various researches are being conducted for reducing these complex noise. In this paper, to remove complex noise, the algorithm processed by modified switching median filter and modified adaptive weighted filter according to the result after judging the kinds of noise is proposed. In the simulation result, excellent denoising capabilities. Furthermore, we compared proposed algorithm with existing methods for objective judgement, and PSNR(peak signal to noise ratio) is used by the criterion of judgement.

Noise processing apparatus and image pickup apparatus

Abstract: An image pickup apparatus includes an image pickup unit which picks up an image of an object so as to generate image data, a noise processing unit which processes dark current noise contained in the image data, and an image processing unit which generates images, based on the image data that have undergone the noise processing. The noise processing unit includes a whitening-out determining unit, a memory, a low-frequency component extracting unit and a subtractor. The memory records data values of dark current noise, and the low-frequency component extracting unit extracts low-frequency components from the data value of dark current noise. The whitening-out determining unit determines whether or not any whitening-out is caused in each pixel, and the subtractor subtracts the low-frequency component for the data value of the pixel having a whitening-out and subtracts the data value of dark current noise for the data values of the other pixels.

Improved Filtering Algorithm for Removing Salt and Pepper Noise

Abstract: Salt and pepper noise is produced at the processes of collection and transmission of digital image.Traditional filtering algorithm can not remove the noise effectively in the high noisy rate.This paper presents a filtering algorithm with accurate noise pixels detection based on extreme and medium filtering algorithm.The algorithm distinguishes noise pixels and signal pixels through setting threshold value and considering the correlativity of adjacent pixels,so it can improve the precision of filtering.Simulation results show that this algorithm is better than other algorithms at filtering salt and pepper noise and preserving the detail of image.And the restoring effect of image is good under high noisy rate.

Image Noise Analysis and Pre-processing for Pattern Match

Abstract: This paper explains an image pre-processing methodology when using a single-camera 3D vision system for a CMM positioning. Three types of noise, mainly caused by 1) electromagnetic interference 2) season, weather and time changes and 3) object position or view angle changes, are classified and analyzed. Based on the analysis pre-processing measures are proposed and employed to minimize the influences of noise. Theoretical analysis and experiments show that the methodology has the advantages of simplicity, efficiency and robustness.

A Study on Cascade Filter Algorithm for Random Valued Impulse Noise Elimination

Abstract: Image signal is corrupted by various noises in image processing, many studies are being accomplished to restore those images. In this paper, we proposed a cascade filter algorithm for removing random valued impulse noise. The algorithm consists two steps that noise detection and noise elimination. Variance of filtering mask and center pixel variance are calculated for noise detection, and the noise pixel is replaced by estimated value which first apply switching self adaptive weighted median filter and finally processed by modified weight filter. Considering the proposed algorithm only remove noise and preserve the uncorrupted information that the algorithm can not only remove noise well but also preserve edge.