The most straightforward way to describe the performance of an image intensifier tube, especially under adverse conditions, is to predict the image it yields. In this work we have developed two different methods to provide realistic simulated images in low light level conditions: 1) Approximate Physical Model. A classical approach based on the simulation of the different degradation sources. It provides a good understanding of the image formation process. 2) Synthesis-by-analysis of real images. The observed noise is modelled through texture analysis tools and the image blur through the MTF. The resulting simulated images for both methods were compared with real intensified images (laboratory chart sights and natural images) taken under controlled conditions, close to the performance limits of the image intensifier tube. Both methods generated good results in terms of visual comparison for different object sizes, contrasts or luminances. These methods can be used as a new tool to predict the performance thresholds of the image intensifier. Only well-known or measurable parameters were used as input for the methods.
Gaussian Scale Mixtures (GSMs) in overcomplete oriented pyramids are, arguably, one of the most powerful available tools for image denoising: 1) they provide a new mathematical frame for modelling the variance-adaptation problem, an approach used in image denoising for the last 25 years; 2) they are applicable to contaminating sources of any spectral density; 3) they yield the smallest L2-norm distortion results in simulations under white Gaussian noise, up to this date; and 4) they allow for a solution, for the first time, to the problem of denoising images affected by unknown covariance noise. In this work, we focus first on the general properties of the GSMs. Then, we review the different ways GSMs have been used in overcomplete oriented pyramids (MAP-z-GSM, BLS-GSM, spatially variant GSM), and their applications: classical denoising, signal-dependent noise removal, unknown covariance noise removal and deblurring.
Image degradation is a frequently encountered problem in different
imaging systems, like microscopy, astronomy, digital photography, etc. The degradation is usually modeled as a convolution with a blurring kernel (or Point Spread Function, psf) followed by noise addition. Based on the combined knowledge about the image degradation and the statistical features of the original images, one is able to
compensate at least partially for the degradation using so-called image restoration algorithms and thus retrieve information hidden for the observer. One problem is that often this blurring kernel is unknown, and has to be estimated before actual image
restoration can be performed. In this work, we assume that the psf can be modeled by a function with a single parameter, and we estimate the value of this parameter. As an example of such a single-parametric psf, we have used a Gaussian. However, the method is generic and can be applied to account for more realistic degradations, like optical defocus, etc.