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.

Improvement of Noise Filtering and Image Registration Methods for the Pressure Sensitive Paint Experiments

Abstract: Image processing procedures for noise reduction and image registration in the Pressure Sensitive Paint (PSP) experiments are investigated. A several types of filter are examined for the removal of shot noise. An algorithm to detect a marker cell located on the model surface is proposed and an appropriate marker size is discussed. The digital processing based on the wavelet transform is effective to reduce the shot noise and to enhance the edge of the model. The algorithm to sharpen the edge of the model using wavelet transforms is developed. The results indicate that the methods developed in this study effectively remove the shot noise and correct the displacement of the model on the images taken during the wind-on.

System and method of corner noise reduction in an image

Abstract: Systems and methods for reducing noise artifacts in an image are disclosed. A radial distance-based look up table may be used to calibrate a noise property change due to the vignette effect. Based on the look up table, a spatial varying noise reduction level is computed to correct the noise property distortion due to the image vignetting.

Steganalysis using Noise Variance Estimation

Abstract: Steganography is the art and science of hiding information in an innocuous medium. Digital imagery is a medium with specific noise characteristics. The devices used to capture a digital image, such as the charge coupled device (CCD) in a digital camera is designed to have a relatively small noise characteristic. This noise characteristic is often reduced by the compression used such as the JPEG standard, resulting in a very low noise media. The addition of steganography to the image has the effect of introducing changes to this "natural" image noise. This paper presents an approach to estimate and model the noise present in an image. Using this estimation, it is shown how steganography introduces detectable changes to this natural noise. This approach is demonstrated on three freely available but difficult to detect embedding techniques, F5, JSteg, and Model-based embedding, and show that it results in features that serve as statistically significant discriminators.

Adaptive Gradient-Based and Anisotropic Diffusion Equation Filtering Algorithm for Microscopic Image Preprocessing

Abstract: In image acquisition process, the quality of microscopic images will be degraded by electrical noise, quantizing noise, light illumination etc. Hence, image preprocessing is necessary and important to improve the quality. The background noise and pulse noise are two common types of noise existing in microscopic images. In this paper, a gradient-based anisotropic filtering algorithm was proposed, which can filter out the background noise while preserve object boundary effectively. The filtering performance was evaluated by comparing that with some other filtering algorithms.

Noise robust classification of moving vehicles via micro-Doppler signatures

Abstract: For robust classification of moving wheeled and tracked vehicles using returned micro-Doppler signals within short dwell time, the influence of receiver white noise and low spectrum resolution are encountered. In this paper, noise reduction and super-resolution are realized simultaneously via a redundant dictionary based l1 -norm optimization method. Experiments based on the measured data are presented, including the analysis of noise reduction performance, and the evaluation of classification robustness for different signal-to-noise ratio cases. The experimental results are also compared with related methods.