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.

Practical experiment on noise perception in noisy images

Abstract: Two noise experiments have been done to determine the perception level of camera noise in dynamic sequences of medical low-dose X- ray images. The X-ray dose per image was at a fluoroscopic level of 1(mu) R/image. In the first experiment an adjustable amount of white noise was mixed with the video output signal of a standard medical X-ray image-intensifier/video-camera imaging chain. The paper explains the difficulties with this experiment and how the second experiment was organized to find more reliable results on noise perception. The second experiment was based on software simulations of both quantum noise and camera noise. The perception threshold was determined with a so-called two- alternative forced-choice method while presenting the noisy image sequences dynamically on a split-screen display. The perception threshold of the camera noise in the image was at a level as low as 27dB below the signal level.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Analysis of effect of noise removal filters on noisy remote sensing images

Abstract: In this work, we have applied various filters on remote sensing images for denoising them. The fundamental key challenge to noise reduction is to reduce or eliminate the noise without failing other aspects of the image. RS (Remote Sensing) Image denoising involves the manipulation of the image data to produce a visually high quality image. There are many kinds of noise that affect on remote sensing images but we have selected only impulsive noise i.e. Gaussian noise and Salt & Pepper Noise. In a simulation we took remote sensing images and analyzed it with an Average filter, Median filter, unsharp filter and Wiener Filter and using statistical quality measures. The analysis of effect of noise removal technique is given in this paper.

Noise Detection and Reduction for Image Sensor by Time Domain Autocorrelation Function Method

Abstract: The present paper describes noise detection and reduction for an imaging system by a method of autocorrelation of pixel data change as a function of time. The random noise level could be detected for each pixel using the proposed method. An algorithm proposed for determining the noise level, and calculating the pixel value by the autocorrelation function value reduced the calculation cost. The proposed method is effective for reducing noise in real time image processing.

Methods and apparatus for noise reduction in digital images

Abstract: Methods and apparatus for reducing or removing noise in digital images. Image noise reduction methods are described that may be applied to grayscale and color images, for example RGB images. An image noise reduction method may, before applying a noise filtering technique, transform the image values from linear space to flat noise space in which the noise is independent of the signal. An edge-preserving noise filtering technique may then be applied to the image in flat noise space. After noise filtering is applied, the image is transformed from flat noise space back to linear space. For color images, the flat noise space may be converted from linear color space to luminance-chrominance space before applying the noise filtering technique so that different filters can be applied to luminance and color channels. After applying the noise filtering technique, the image is converted back to linear color space.

On the robust technique of mixed Gaussian and impulsive noise reduction in color digital images

Abstract: In this paper a new technique of mixed Gaussian and impulsive noise suppression in color images is proposed. The novel approach is based on a weighted average of pixels contained in the filtering block. The main novelty of the proposed solution lies in the new definition of similarity between a pixel and samples belonging to a small window centered at the central pixel of the processing block. Instead of direct comparison of pixels, a measure based on the similarity between a given pixel and the samples from the window is utilized. This measure is defined as the sum of distances in a given color space between a pixel and a certain number of most similar pixels from this window. In this way, the new similarity measure is not influenced by the outliers injected into the image by the impulsive noise process and the averaging process ensures the effectiveness of the new filter in the reduction of Gaussian noise. The experimental results prove that the novel filtering design is capable of suppressing even strong mixed noise and is competitive with respect to state-of-the-art methods.