By KS Thyagarajan
With crystal readability, this publication conveys the most up-tp-date rules in electronic photograph processing, delivering either the heritage thought and the sensible functions to varied industries, similar to electronic cinema, video compression, and streaming media.
* study the function of human imaginative and prescient in snapshot visualization
* comprises the MATLAB codes used to generate lots of the figures and tables indexed within the booklet, in addition to a couple of MATLAB projects
* Case reviews although out illustrate the sensible program of the speculation
Read or Download Digital Image Processing with Application to Digital Cinema.(Focal Press) PDF
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Extra resources for Digital Image Processing with Application to Digital Cinema.(Focal Press)
2-28) Otherwise H(W1,W2) is said to be nonseparable. Generally, the two 1-D functions in Eq. (2-28) are identical. The advantages in using separable FIR filters are (1) the 2-D filtering can be implemented as a row–column operation, namely an image can be filtered first along its rows and then along the columns of the row-filtered image, and (2) the implementation can be more efficient. The row–column operation can be shown as follows. Using the 2-D convolution in Eq. (2-6) and the condition for separability in Eq.
Define W2 n W = W12 + W 22 , f = tan -1 , and q = tan -1 . 3 p Ú 0 WJ 0 (W m 2 + n 2 )dW 2 Two-Dimensional Signals, Systems, and Discrete Fourier Transform 42 where J0(x) and J1(x) are the Bessel functions of the first kind of orders 0 and 1, respectively. 2), 20 < D < 60 a =Ì , D < 20 Ó0, (2-56) D = -20 log d pd s (2-57) where and dp and ds are the passband and stop band ripples, respectively, as shown in Figure 2-11. 3p −p FIGURE 2-11 A circularly symmetric 2-D Kaiser window of Example 2-7. 6 FIR Filter Design 43 obtained as h[m,n] = hI[m,n]wc[m,n], |m| £ 4, |n| £ 4.
01. The parameter a is determined from Eq. 3953. 01 FIGURE 0 Wp Ws p W A frequency response tolerance scheme. 125 and then N from Eq. (2-45). 855. 125 the filter length and so M = 9. 375p . Using the Kaiser window parameters just found, we now 2 obtain the window function wk[n], -9 £ n £ 9. Finally, the desired filter impulse response is determined from Df = h d [n ] = w K [n ]. sin(nW c ) , - 9 £ n £ 9. np Figure 2-10a shows a plot of the Kaiser-windowed FIR filter impulse response. The corresponding magnitude response is shown in Fig.