MATLAB Image Processing _ homomorphic filtering, matlab Image Processing homomorphic

Source: Internet
Author: User

MATLAB Image Processing _ homomorphic filtering, matlab Image Processing homomorphic

Homomorphic filtering: uses the generalized superposition principle to filter the homomorphic system. Homomorphic filtering is an image processing method that combines frequency filtering and grayscale transformation. It relies on the image illumination/reflectivity model as the basis for processing in the frequency domain, the image quality is improved by compressing the brightness range and enhancing the contrast. This method can make image processing conform to the non-linear characteristics of human eyes for brightness response, avoiding the distortion of direct Fourier transformation processing on the image. The basic principle of homomorphic filtering is to regard the gray value of a pixel as the product of two components: illumination and reflectivity. Because the illumination is relatively small, it can be regarded as the low-frequency component of the image, while the reflectivity is the high-frequency component. The effects of illumination and reflectivity on Pixel gray-scale values are processed separately to reveal detailed features in the shadow area.
The basic process of homomorphic filtering is as follows: S (x, y) ----> Log ----> DFT ----> frequency domain filtering ----> IDFT ----> Exp ----> T (x, y) S (x, y) indicates the original image, T (x, y) indicates the processed image, and Log indicates the logarithm operation; DFT represents the Fourier Transformation (in practice, the fast Fourier transformation FFT is used); IDFT represents the Fourier inverse transformation (in practice, the fast Fourier inverse transformation IFFT is used); Exp represents the exponential operation.
Below is a typical difference between homomorphic filtering and other image processing methods:

Implementation Code:

Function I3 = test_tontai (I) I = double (rgb2gray (I); [M, N] = size (I); rL = 0.5; rH = 4.7; % You can adjust the parameter c = 2; d0 = 10; I1 = log (I + 1); % to retrieve the logarithm FI = fft2 (I1 ); % Fourier transform n1 = floor (M/2); n2 = floor (N/2); for I = 1: M for j = 1: n d (I, j) = (i-n1 ). ^ 2 + (j-n2 ). ^ 2); H (I, j) = (rH-rL ). * (exp (c * (-D (I, j ). /(d0 ^ 2) + rL; % Gaussian homomorphic filter endendI2 = ifft2 (H. * FI); % Fourier inverse transformation I3 = real (exp (I2); subplot (122), imshow (I3, []); title ('post-homomorphic filter enhancement ');

Experiments show that homomorphic filtering is similar to high-dynamic range compression. For example, you can highlight the hidden part of the image.


The original image is a low-exposure image:



After homomorphic filtering:



Note: original article, reproduced please indicate the source: http://blog.csdn.net/scottly1/article/details/42705271


Related Article

Contact Us

The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion; products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the content of the page makes you feel confusing, please write us an email, we will handle the problem within 5 days after receiving your email.

If you find any instances of plagiarism from the community, please send an email to: info-contact@alibabacloud.com and provide relevant evidence. A staff member will contact you within 5 working days.

A Free Trial That Lets You Build Big!

Start building with 50+ products and up to 12 months usage for Elastic Compute Service

  • Sales Support

    1 on 1 presale consultation

  • After-Sales Support

    24/7 Technical Support 6 Free Tickets per Quarter Faster Response

  • Alibaba Cloud offers highly flexible support services tailored to meet your exact needs.