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Image compression

Where rQ is the apex radius of the specimen. In practice the ion trajectories are curved, due to the presence of the specimen shank, and the magnification is reduced by an image compression factor, (3, and... [Pg.5]

There is a number of alternative Raman imaging techniques these include using the Hadamard transform technique [25-27], and such as fibre-bundle image compression, which however is not yet commercially available [26-31]. However in the latter approach, the laser power on the sample could be high, since the beam is not defocused, and the possibility of sample damage increases. [Pg.534]

Gift, A. D., Ma, J., Haber, K. S., McClain, B. L., and Ben-Amotz, D. (1999), Near-infrared Raman imaging microscope based on fiber-bundle image compression, J. Raman Spectrosc., 30,757-765. [Pg.431]

Methods developed for traditional lossy image compression can be used in the compression of multispectral images. The discrete cosine transform (DCT),... [Pg.153]

Fractals are of great interest because of their exquisite combination of beauty, complexity, and endless structure. They are reminiscent of natural objects like mountains, clouds, coastlines, blood vessel networks, and even broccoli, in a way that classical shapes like cones and squares can t match. They have also turned out to be useful in scientific applications ranging from computer graphics and image compression to the structural mechanics of cracks and the fluid mechanics of viscous fingering. [Pg.398]

Figu re 2.n Resolution of images compressed in the spectral direction and in the spatial direction. Recovery of full C and ST matrices. [Pg.95]

Figure 2.12 Left Resolution results after image compression by binning in the spectral direction. Center Resolution results from full image analysis. Right Resolution results after Image compression in the pixel direction by taking a pixel subset around SIMPLISMA purest selected pixels. Figure 2.12 Left Resolution results after image compression by binning in the spectral direction. Center Resolution results from full image analysis. Right Resolution results after Image compression in the pixel direction by taking a pixel subset around SIMPLISMA purest selected pixels.
In Section 2 we introduce the fundamentals of image compression and overview the various compression algorithms. We review the transformation techniques used in image compression in Section 3. Section 4 describes image compression using optimal task-based and best-basis image compression algorithms. [Pg.458]

When comparing different lossy image compression algorithms one usually desires the compressed image to be of the same visual quality as the original image. The most common measure of quality is the mean square error (MSE) or distortion defined as ... [Pg.461]

Integrated task-specific wavelets and best-basis search for image compression... [Pg.473]

As stated in the previous section, optimal wavelet image compression can be achieved by integrating the process of wavelet construction with best-basis search. The best-basis search using a standard off-the-shelf Coifman wavelet (Nf = 2) for four levels is shown in Fig. II. In this case the threshold cost function used was simply the constant value 0.2. The resulting compression ratio obtained was 9.50. [Pg.473]

K. Culik and J. Kari, Image compression using weighted finite automata. Comput. and Graphics, 17 (1993), 305-313. [Pg.477]

M. Mandal, S. Panchanathan and T. Aboulnasr, Choice of wavelets for image compression. In Information Theory and Applications 11, Lecture Notes in Computer Science LNCS 1133, (P. Fortier, J.Y. Chouinard and T. Gulliver (Eds) Springer-Verlag, (1996), 239-249. [Pg.478]

Another application inherently associated with image de-noising is image compression. Image compression, similar to de-noising, removes unimportant or undesired details from an image, and thus compresses it. There exist a variety of data compression techniques in different application areas, many of which have been well standardised. Data compression techniques can be... [Pg.481]

An effective approach to data compression using wavelets was introduced by Wickerhauser [33]. Data coding is one of the most visible applications of wavelets. Compression ratios of about 10 1 can be achieved without significant loss of visual detail. The FBI has adopted a standard for digital fingerprint image compression, based on wavelet compression algorithms (see Fig. 18). This standard is described in the work of Bradley [34] and Brislawn and Hopper [35,36]. [Pg.508]


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See also in sourсe #XX -- [ Pg.71 ]

See also in sourсe #XX -- [ Pg.459 ]




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Compression of images

Integrated task-specific wavelets and best-basis search for image compression

Resolution compressed image

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