Image Filtering Using Fast Convolution in .NET

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15.8 Image Filtering Using Fast Convolution
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A 720 480 image s RGB color space consists of 720 480 3 data samples. For the NTSC motion video at 30 frames per second, there will be 720 480 3 30 = 31 104 000 bytes/s. As the image ltering requires four nested loops for each pixel and every lter coef cient in the kernel, this can be very computational intensive for large-size image even by using a 3 3 lter. An alternative approach is to use fast convolution method. The 2-D fast convolution using the 2-D FFT is considerably fast for ltering a large-size image because the spatial-domain convolution becomes multiplication in frequency domain. If the image signal is orthogonal, we can further reduce computational requirements by applying lter in one direction at a time. Given a function f (m, n) of two spatial variables m and n, the 2-D M-by-N DFT F(k, l) is de ned as F(k, l) =
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M 1 N 1 m=0 n=0
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f (m, n)e j(2 /M)km e j(2 /N )ln ,
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(15.14)
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where k and l are frequency indices for k = [0, M 1] and l = [0, N 1], respectively. The 2-D inverse DFT is de ned as f (n, m) = 1 MN
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M 1 N 1 k=0 l=0
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F(k, l)e j(2 /M)km e j(2 /N )ln .
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(15.15)
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PRACTICAL APPLICATIONS
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MATLAB Image Processing Toolbox provides a 2-D FFT function fft2 and an inverse FFT function ifft2. The 2-D fast convolution can be computed using the following steps: 1. The lter coef cient matrix and image data matrix must have the same dimensions. If one matrix is smaller than the other, we can pad zeroes on the smaller matrix. 2. Compute the 2-D FFT of both the image and coef cient matrices using the MATLAB function fft2. 3. Multiply the frequency-domain coef cient matrix with image matrix using dot product. 4. Apply the inverse 2-D FFT function ifft2 to obtain the ltered results.
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Example 15.8: Instead of performing 2-D convolution, the frequency-domain ltering uses multiplication. The use of MATLAB functions fft2 and ifft2 on RGB images can be expressed as
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fft2R = fft2(double(RGB(:,:,1))); [imHeight imWidth] = size(fft2R); fft2Filt = fft2(coeff, imHeight, imWidth); fft2FiltR = fft2Filt .* fft2R; newRGB(:,:,1) = uint8(ifft2(fft2FiltR));
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Here, fft2FiltR is the 2-D fast convolution result of the red components. The lter coef cients are zero padded and the frequency-domain matrix is obtained using the fft2 function. The symbol .* in the fourth line of MATLAB script is the dot-product operator. Figure 15.14 shows the 2-D ltering results. The fast convolution results (Figure 15.14) show that when dealing with a large image, the ltering can be performed in frequency domain. The computational requirement for fast convolution is much less than the computation in the spatial domain using the 2-D convolution.
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15.9 Practical Applications
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Image processing has been used in many practical applications. More and more images we have today become larger and larger in size. Therefore, image compression plays a key role in image applications. Many international standards, such as JPEG, are commonly used for ef cient storage and transmission. JPEG can achieve up to 10:1 compression ratio.
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15.9.1 JPEG Standard
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JPEG compression is de ned in the ITU-T Recommendation T.81. JPEG le format has been widely used in printing, digital photography, video editing, security, and medical imaging applications. Figure 15.15 shows the basic functions of JPEG encoder. In JPEG encoding process, the image pixels are grouped into 8 8 blocks. Each block is transformed by a forward DCT to obtain 64 DCT coef cients. The rst coef cient is called the DC coef cient, and the rest coef cients are referred as AC coef cients. These 64 DCT coef cients are quantized by a quantizer using one of 64 corresponding values from a quantization table speci ed by ITU-TT.81.
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