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 brief study of various noise model and filtering techniques

Abstract: In image processing, noise reduction and restoration of image is expected to improve the qualitative inspection of an image and the performance criteria of quantitative image analysis techniques Digital image is inclined to a variety of noise which affects the quality of image. The main purpose of de-noising the image is to restore the detail of original image as much as possible. The criteria of the noise removal problem depends on the noise type by which the image is corrupting .In the field of reducing the image noise several type of linear and non linear filtering techniques have been proposed . Different approaches for reduction of noise and image enhancement have been considered, each of which has their own limitation and advantages.

Random resampling masks: a non-Bayesian one-shot strategy for noise reduction in digital holography

Abstract: Holographic imaging may become severely degraded by a mixture of speckle and incoherent additive noise. Bayesian approaches reduce the incoherent noise, but prior information is needed on the noise statistics. With no prior knowledge, one-shot reduction of noise is a highly desirable goal, as the recording process is simplified and made faster. Indeed, neither multiple acquisitions nor a complex setup are needed. So far, this result has been achieved at the cost of a deterministic resolution loss. Here we propose a fast non-Bayesian denoising method that avoids this trade-off by means of a numerical synthesis of a moving diffuser. In this way, only one single hologram is required as multiple uncorrelated reconstructions are provided by random complementary resampling masks. Experiments show a significant incoherent noise reduction, close to the theoretical improvement bound, resulting in image-contrast improvement. At the same time, we preserve the resolution of the unprocessed image.

Image Processing In Signal-Dependent Noise

Abstract: Image detection noise is a fundamental limitation in picture processing, whether analog or digital. This noise is characteristically signal-dependent and this signal-dependence introduces significant problems in the design of appropriate noise-suppression techniques. This paper outlines some recent results obtained by the authors in the optimum suppression of two types of signal-dependent image noise: film-grain noise and photoelectron shot noise. The work in grain noise suppression involves deriving the minimum-mean-square error Wiener filter for a new form of signal-dependent noise model suggested in earlier work by T. S. Huang. Implementation of these filters by either coherent optical or digital processing techniques is possible. Digital computer simulations of grain noise suppression using two particular cases of this additive, "signal-modulated" noise model were performed. They demonstrate the potential advantages of noise suppression filters which make use of a priori knowledge of the signal-dependent nature of the grain noise. The results of work on linear, unbiased restoration of images recorded in the presence of photoelectron noise are summarized. Additional work in both of these areas is suggested, with a particular need existing for correlating the properties of various models proposed for grain noise with experimental data obtained on emulsions using scanning microdensitome ters.

An Efficient Adaptive Fuzzy Switching Weighted Mean Filter for Salt-and-Pepper Noise Removal

Abstract: An image degraded by noise is a common phenomenon. In this letter, we propose a novel adaptive fuzzy switching weighted mean filter to remove salt-and-pepper (SAP) noise. The process of denoising includes two stages: noise detection and noise elimination. In the first stage, pixels in a corrupted image are classified into two categories: original pixels and possible noise pixels. For the latter, we compute the maximum absolute luminance difference of processed pixels next to possible noise pixels to classify them into three categories: uncorrupted pixels, lightly corrupted pixels, and heavily corrupted pixels. In the second stage, under the assumption that pixels at a short distance tend to have similar values, the distance relevant weighted mean of the original pixels in the neighborhood of a noise pixel are computed. For a nonnoise pixel, retain it as unchanged; for a lightly corrupted pixel, replace it with the weighted average value of the weighted mean and its own value; and for a heavily corrupted pixel, change it to be the weighted mean. Experimental results show that compared to some state-of-the-art algorithms, our method keeps more texture details and is better at removing SAP noise and depressing artifacts.