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.

Noise and zero point drift in 1.7μm cutoff detectors for SNAP

Abstract: We present the results of a detailed study of the noise performance of candidate NIR detectors for the proposed Super-Nova Acceleration Probe. Effects of Fowler sampling depth and frequency, temperature, exposure time, detector material, detector reverse-bias and multiplexer type are quantified. We discuss several tools for determining which sources of low frequency noise are primarily responsible for the sub-optimal noise improvement when multiple sampling, and the selection of optimum fowler sampling depth. The effectiveness of reference pixel subtraction to mitigate zero point drifts is demonstrated, and the circumstances under which reference pixel subtraction should or should not be applied are examined. Spatial and temporal noise measurements are compared, and a simple method for quantifying the effect of hot pixels and RTS noise on spatial noise is described.

Color image noise removal by modified adaptive threshold median filter for RVIN

Abstract: In the field of digital image processing impulse noise removal from color images is one of the most challenging tasks for researchers. A Modified Adaptive Threshold Median Filter (MATMF) is presented for impulse noise removal for both low and high-density noise levels. In this paper, we have proposed filter for color image random valued impulse noise reduction. For color images, impulse noise removal there are two main stages, firstly, the detection of the impulse noises on the basis of maximum and minimum value of pixels in a small window. In the second stage removal of noise on the basics median calculation. In the filtering stage, the noise-free pixels remain unchanged in all RGB frames, windows and noisy pixels are restored using a median filter. Experimental results show that proposed method is superior to the conventional methods in Peak Signal Noise Ratio [8] (PSNR) values and Mean Square Error (MSE) of the image performs, with and without noise compared. The mathematical analysis describes that the analysis of the noisy pixels and use of noise-free pixels for the de-noising purpose provide much better results and provides better visual quality of de-noised image and provide good quality in human persecution.

Image processing device, image sensing apparatus, and image processing method

Abstract: An image processing device is comprised of: a frequency component resolution section for resolving an image obtained from an image sensor having a light-shielded pixel area and a non-light-shielded pixel area into two or more frequency components; a noise amount calculation section for calculating a noise amount for the frequency component based on the frequency component in the light-shielded pixel area; a noise suppression section for suppressing the noise component for the frequency component in the non-light-shielded pixel area according to the noise amount that has been calculated by the noise amount calculation section; and a frequency component synthesis section for synthesizing the frequency component that has been resolved by the frequency component resolution section to thereby form an image.

Method of image noise reduction processing

Abstract: A method of noise reduction processing for reducing noise generated when an image of a photographic film is converted to a digital image signal includes the steps of: measuring a large area transmission density of each of a plurality of image frames recorded on the photographic film; classifying measured values of the large area transmission density into a plurality of groups; reading the image signals of the same image frame by an amount corresponding to the number of inputs of the image signals of the same image frame set in advance for each of the classified groups; and subjecting the image signals of the same image frame which have been read to averaging processing.

Method for sharpening a digital image with signal to noise estimation

Abstract: A method of sharpening a digital image having image pixels according to its noise content, includes the steps of providing an image sharpener having a variable parameter of sharpening; generating a noisy pixel belief map corresponding spatially to the image pixels having belief values indicating the likelihood that the modulation about respective pixels are due to system noise; and using the noisy pixel belief map to vary the parameter of the image sharpener.