Download e-book for iPad: Advanced Digital Imaging Laboratory Using MATLAB by Leonid P Yaroslavsky

By Leonid P Yaroslavsky

ISBN-10: 0750310502

ISBN-13: 9780750310505

No matter if you're learning or already utilizing electronic imaging options, constructing skillability within the topic isn't attainable with out getting to know useful abilities. during this ebook, Prof. Yaroslavsky grants an entire utilized path in electronic imaging geared toward complex scholars and practitioners. protecting all parts of electronic imaging, the textual content presents an overview of outlying ideas of every subject whereas delivering greater than eighty MATLAB® dependent workouts. matters addressed embody picture digitization (discretization, quantization, compression), electronic snapshot formation and computational imaging, photo resampling and construction non-stop photo types, photo and noise statistical characterization and diagnostics, statistical snapshot types and trend formation, photograph correlators for localization of items, equipment of photo perfecting (denoising, deblurring), and techniques of picture enhancement. Key beneficial properties comprise: helps * learning of all points of electronic imaging from picture sign digitization to photo parameter estimation, restoration, recovery and enhancement. * MATLAB resource codes for routines are supplied, which readers can adjust for his or her specific wishes and tastes, to layout new workouts and, additionally, to take advantage of them for fixing specific image-processing initiatives. * try out signs and photographs supplied within the publication, in addition to method of the experiments, might be necessary for readers of their additional reports and sensible paintings. * workouts are supported via outlines of the corresponding thought. The ebook deals a distinct mix of workouts, supportive software program and knowledge set that may be used not just for learning the topic, yet in additional sensible paintings.

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If the ‘backpropagation’ transform is applied to the recorded hologram in this way, real and imaginary object images overlay each other in the reconstructed image. In order to avoid this overlay, one needs to be able to separate amplitude and phase components in the recorded hologram. To enable this, ‘on-axis’ holograms are recorded in several exposures (usually three or four) with appropriate changes of the reference beam phase. The amplitude and phase components of the recorded wavefront are then numerically separated from these recordings.

In order to speed up the iterative image reconstruction it is advisable to introduce a certain redundancy to the number of available pixels with respect to the number of image non-zero spectral coefficients (the area of the pie-sector band-limiting mask). The recommended redundancy is of the order of the ratio 4/π of the area of a square to the area of a pie sector inscribed into this square. 3 exemplifies how the results of the exercise are presented. 2. An example of a run of the exercise ‘Image recovery from sparse or non-uniformly sampled data’.

5. How would you describe aliasing artifacts in the reconstruction of Fresnel holograms using the Fourier reconstruction algorithm for μ2 < 1? 6. How does the error of image reconstruction from projections depend on the number of projections? 7. Were errors of image reconstruction from projections more or less uniform for all tested images? If not, why might this be? 1 Introduction Accurate and fast image resampling is a key operation in many digital image processing applications. A few examples are multi-modality data fusion, image mosaicing, image reconstruction from projections, image super-resolution from sequences of image frames, stabilization of videos distorted by turbulence, target location and tracking with sub-pixel accuracy.

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Advanced Digital Imaging Laboratory Using MATLAB by Leonid P Yaroslavsky

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