Showing papers on "Dark-frame subtraction published in 1977"

Image restoration by Wiener filtering in the presence of signal-dependent noise.

Abstract: An optimum filter to restore the degraded image due to blurring and the signal-dependent noise is obtained on the basis of the theory of Wiener filtering. Computer simulations of image restoration using signal-dependent noise models are carried out. It becomes clear that the optimum filter, which makes use of a priori information on the signal-dependent nature of the noise and the spectral density of the signal and the noise showing significant spatial correlation, is potentially advantageous.

Design Considerations for Digital Image Processing Systems

Abstract: The brightness levels in a scene or image, together with the spatial relationships among these levels, comprise the total input and output data available to the digital image processor. Indeed, in any mathematical image processing operation, these are the elements of the image which are being manipulated and which represent the major concern of the image processing professional. However, many other factors affect image brightness relations and can distort or obscure the outcome of any image processing experiment. These factors, which consist of a long chain of transmitters, transducers, signal conditioners and processors, are in aggregate commonly called the image chain. An understanding of the image chain is essential to the design of image processing systems.

Fast algorithm for noise smoothing based on a subjective criterion

Abstract: A simple and fast algorithm is presented to remove selectively random noise from an image while retaining visual resolution.

Noise problems in optical image processing

Abstract: Noise problems in image processing by optical filtering are examined A comparison between coherent and incoherent illumination is made

Image Noise and Smoothing in Computed Tomography (CT) Scanners

Abstract: The question of what spatial frequency response gives optimum image quality in computed tomography (CT) images is studied. We have developed a technique for smoothing these images by using a modified back projection filter with attenuated high frequency response. The results are images with a blurred appearance but dramatically reduced image noise. On processing raw data from the EMI head scanner in this way, grey and white matter distributions are clearly visualized with no increase in radiation dose. These results are analyzed in terms of the Weiner spectrum and correlation properties of CT noise. In particular, a "noise equivalent aperture" is defined in a way analogous to that of Shade. It is shown that the prediction of the simple Rose model of noise sampling can not explain the improvement produced by smoothing, even when the correlated character of the noise is taken into account.