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Table 15.14 rptblocal widthLoop-1 mov *AR3, AC0 bfxtr #0xff00, AC0, T0 and #0x00ff, AC0, T0 || mov *AR2(T0), AC1 mov *AR2(T0), AC0 or AC1<<#8, AC0 mov AC0, *AR3+ widthLoop: nop heightLoop: ret ; ; ; ; (continued)
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This experiment is controlled using the MATLAB script histogramEqualization.m. Table 15.15 lists the les used for this experiment. Procedures of the experiment are listed as follows: 1. Connect the C5510 DSK or simulator to the computer. 2. Start MATLAB and set the MATLAB working directory to the directory ..\exp15.10. 5_histogramEQ where the MATLAB script histogramEqualization.m is located. 3. Run the experiment using the MATLAB script and observe the resulting images. The equalized image has a larger dynamic range. 4. The histogram equalization is a useful tool for adjusting underexposed images especially for the B&W images. Try different underexposed and overexposed images and observe the histogram equalization results.
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15.10.6 2-D Image Filtering
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In this experiment, we introduce the basic image ltering using a 3 3 lter kernel. Image ltering requires extremely high computational power because of the large amount of pixels. For real-time applications,
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Table 15.15 Files histogramEqualization.m histEqTable.c histogramInit.c histogramEqTest.c histogram.asm histEqualizer.asm histogramEQ.h histogramEQ.cmd histogramEQ.pjt hallway.jpg street.jpg File listing for experiment exp15.10.5_histogramEQ Description MATLAB script controls the experiment Fixed-point function computes histogram EQ table Fixed-point function for initialization Program for testing experiment C55x assembly routine computes histogram C55x assembly routine performs equalization C header le DSP linker command le DSP project le Image le Image le
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Table 15.16 C code for 2-D ltering of image
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for(x=0; x<imageWidth; x++) { for(m=0; m<M; m++) { for(n=0; n<N; n++) { temp32 += (unsigned long)pixel[row++][x] * filter[n][m]; if (row == 3) { row = 0; } } } }
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the image ltering must be implemented ef ciently using assembly routines, or we should rely on speci c hardware accelerator to meet the real-time constraints.
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This experiment uses the basic 3 3 kernel as the image lter to achieve different effects as shown in Example 15.7. The implementation of the 2-D ltering de ned in Equation (15.13) is listed in Table 15.16. In Table 15.16, the input image located at position x of row in the data array, pixel[row][x], is ltered by an n-by-m 2-D lter filter[n][m]. The middle pixel corresponds to the current input. The input and its neighboring pixels are all contributing to the ltering results. To utilize the limited DSK memory, this experiment uses three rows of input image data for the 3 3 image lter kernel. These rows are arranged as shown in Figure 15.22. The processing starts with three rows of data. The current input and output are represented by index n as shown in Figure 15.22(a). The row indexed with n 1 contains the previous row of image data and n + 1 contains the next row. The ltering process continues from the rst column to the last column for each row. The update of the data buffer is carried one row at a time. The update of the lter delay line (three rows) is actually achieved by adjusting the 2-D data array index, row. As shown in Figure 15.22(a), row n is the current center row when the ltering process starts. After the rst update, the new current row is labeled with index n + 1 (see Figure 15.22(b)). After another update, the current row is n + 2 as shown in Figure 15.22(c). This pattern is repeated. The ltering process is depicted in Figure 15.12. It computes the dot product starting from columns then rows between the 3 3 data block and the 3 3 lter kernel.
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Image row: n 1 (previous) Image row: n (current)
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