Digital infra-red image processing

This paper describes a general approach to the digital processing of infra-red images, from data acquisition to data use. The main steps of this processing are developped . Emphasis is placed on the pre-processing which reduces the systematic radiometric errors and the considerable random noise inherent to infra-red images. It is then shown that, with such a pre processing, classical techniques used for image in the visible domain become practicable . Each process is explained in theory and illustrated with a practical example.Cet article présente une approche générale du traitement numérique des images infrarouges de l'acquisition à l'utilisation des données. Les principales étapes d'un tel traitement y sont développées . L'accent est mis sur les prétraitements qui visent à diminuer les erreurs radiométriques et le fort bruit aléatoire qui caractérisent les images infrarouges . Il est ensuite montré comment l'application de tels prétraitements permet d'utiliser efficacement des méthodes classiquement mises en oeuvre pour des images du domaine visible . Chaque traitement, expliqué d'un point de vue théorique, est illustré par un exemple concret

A Non-Interactive Approach to Land Use Determination

In this paper, we report on an operational procedure for use by the Corps of Engineers to acquire land use information for hydrologic planning purposes. The operational constraints preclude the use of dedicated, interactive image processing facilities. The procedure, which is summarized in detail, combines manual interpretation techniques and the batch-mode computer analysis of Landsat digital data. An example of the application of the procedure to an urban watershed is described

Computationally efficient nonlinear edge preserving smoothing of n-D medical images via scale-space fingerprint analysis

Nonlinear edge preserving smoothing often is performed prior to medical image segmentation. The goal of the nonlinear smoothing is to improve the accuracy of the segmentation by preserving changes in image intensity at the boundaries of structures of interest, while smoothing random variations due to noise in the interiors of the structures. Methods include median filtering and morphology operations such as gray scale erosion and dilation, as well as spatially varying smoothing driven by local contrast measures. Rather than irreversibly altering the image data prior to segmentation, the approach described here has the potential to unify nonlinear edge preserving smoothing with segmentation based on differential edge detection at multiple scales. The analysis of n-D image data is decomposed into independent 1-D problems that can be solved quickly. Smoothing in various directions along 1-D profiles through the n-D data is driven by a measure of local structure separation, rather than by a local contrast measure. Isolated edges are preserved independent of their contrast, given an adequate contrast to noise ratio

Comparison of wavelet image coders using the Picture Quality Scale (PQS)

Image coding is one of the most visible applications of wavelets. There has been increasing number of reports each year since the late 1980's on the design of new wavelet coders and variations to existing ones. In this paper, we report some results from our comparative study of wavelet image coders using a perception-based, quantitative picture quality scale as the distortion measure. Coders are evaluated in rate-distortion sense; the influences of different wavelets, quantizers, and encoders are assessed individually. Our results provide an insight into the design issues of optimizing wavelet coders, as well as a good reference for application developers to choose fro