By Leonid P Yaroslavsky
Even if you're learning or already utilizing electronic imaging ideas, constructing skillability within the topic isn't really attainable with no getting to know useful abilities. during this ebook, Prof. Yaroslavsky provides a whole utilized path in electronic imaging aimed toward complex scholars and practitioners. masking all parts of electronic imaging, the textual content offers an overview of outlying rules of every subject whereas providing greater than eighty MATLAB® dependent workouts. matters addressed embody picture digitization (discretization, quantization, compression), electronic photograph formation and computational imaging, photo resampling and construction non-stop snapshot types, photo and noise statistical characterization and diagnostics, statistical snapshot types and trend formation, photo correlators for localization of items, tools of photograph perfecting (denoising, deblurring), and techniques of picture enhancement. Key positive aspects comprise: helps * learning of all points of electronic imaging from snapshot sign digitization to photograph 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 routines and, additionally, to take advantage of them for fixing specific image-processing initiatives. * try out signs and pictures supplied within the ebook, in addition to technique of the experiments, might be necessary for readers of their additional reports and functional paintings. * workouts are supported through outlines of the corresponding concept. The ebook bargains a distinct mix of workouts, supportive software program and knowledge set that may be used not just for learning the topic, yet in extra useful paintings.
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In chapter 8 (‘Methods of image perfection’) and chapter 9 (‘Methods of image enhancement’), we will see that local image histograms computed for other types of pixel neighborhood are also important for implementing image nonlinear ﬁlters. The exercise ‘Image histograms’ provided in this section enables comparison of global (frame-wise) histograms for different images as well as observation of the variability of the image local histogram. The latter is achieved by means of visualizing mean square deviations and maximal deviations of image local histograms from the local histogram of the user-selected image fragment.
3. What is your estimate of how much lower the standard deviation of DPCM prediction errors is than that of the corresponding images, and how much one can expect to gain in terms of the required number of quantization levels? 4. Is 2D DPCM substantially more efﬁcient in terms of image data compression than 1D DPCM? 5. Are 1D and 2D DPCM equally vulnerable to communication channel noise? 6. What is the main difference between the effects of communication channel noise in 1D DPCM and 2D DPCM? 7. What would be your recommendation regarding selecting the block size for DCT transform coding for a given particular image?
14. Rotation error spectra: pseudo-random test image with uniform DFT spectra. 15. Image iterative zoom-in/zoom-out exercise: images (left panel) and their DFT spectra (right panel) displayed as gray scale images centered at the spectrum DC component with the most intensive spectral components shown bright and the least intensive components shown dark. 6 Comparison of signal numerical differentiation and integration methods In the exercise ‘Comparison of signal numerical differentiation and integration methods’ one can compare the performance of traditional numerical signal 4-11 Advanced Digital Imaging Laboratory Using MATLAB® differentiation and integration methods and DFT/DCT-based methods that, implicitly, assume signal discrete sinc interpolation and, as such, realize perfect numerical differentiation and integration.
Advanced Digital Imaging Laboratory Using MATLAB by Leonid P Yaroslavsky