Convolution, Fourier Transform, S-Transform, and Z-Transform
This article covers fundamental signal forms, convolution, Fourier transform, s-transform, and z-transform, with code implementation insights for signal processing techniques.
Image Processing in the Frequency Domain Using Fourier Transform Methods
Implementation of image processing in the frequency domain using Fourier transform techniques, comparing differences between image restoration from magnitude and phase components, with successful debugging and validation
Digital Image Processing Series Programs
This series provides implementation code with detailed explanations for various digital image processing techniques: P0301: Displaying digital image matrix data and performing Fourier transforms using MATLAB's fft2 function P0302: Image compression through 2D Discrete Cosine Transform (DCT) implementation P0303: Contrast enhancement using grayscale transformation methods with intensity mapping P0304: Histogram equalization algorithm for image enhancement P0305: Simulating image degradation with Gaussian white noise and salt-and-pepper noise using imnoise function P0306: Filtering salt-and-pepper noise using 2D median filtering function medfilt2 P0307: Mean filtering of noisy images using MATLAB's filter2 function with averaging kernels P0308: Adaptive Wiener filtering for image restoration P0309: Image sharpening using five different gradient enhancement methods including Sobel and Prewitt operators P0310: High-pass filtering and mask processing techniques
Display of Digital Image Matrix Data and Its Fourier Transform
Program Code Descriptions for Image Processing Algorithms: P0301 - Display of digital image matrix data and Fourier transform implementation using fft2 function; P0302 - Image compression via 2D discrete cosine transform (DCT) with dct2 function; P0303 - Image contrast enhancement through grayscale transformation techniques; P0304 - Histogram equalization for image enhancement; P0305 - Simulation of Gaussian white noise and salt-and-pepper noise effects on images; P0306 - Salt-and-pepper noise filtering using medfilt2 2D median filtering function; P0307 - Mean filtering of noisy images with MATLAB's filter2 function; P0308 - Adaptive Wiener filtering for image restoration; P0309 - Image sharpening using five different gradient enhancement methods; P0310 - High-pass filtering and mask processing for images; P0311 - Image smoothing with Butterworth low-pass filters
Implementation of 2D Image Fourier Transform Algorithm Using MATLAB
Implementation of 2D image Fourier transform algorithm using MATLAB with code-level explanations for frequency domain analysis and processing
Display of Digital Image Matrix Data and Its Fourier Transform
Techniques covered include: Display of digital image matrix data and its Fourier transform, image compression using 2D discrete cosine transform, contrast enhancement through grayscale transformation methods, histogram equalization. Simulation of Gaussian white noise and salt-and-pepper noise effects on images, filtering salt-and-pepper noise using MATLAB's medfilt2 function for 2D median filtering, mean filtering of noisy images using filter2 function, adaptive Wiener filtering for images, image sharpening using five different gradient enhancement methods, high-pass filtering and mask processing techniques, smoothing noisy images with Butterworth low-pass filters, and image sharpening using Butterworth high-pass filters.
Correlation Function Method
The correlation function method estimates the autocorrelation function R from sequences, then applies Fourier transform to obtain power spectrum estimation. This approach provides strong estimation accuracy when the time delay is relatively small compared to the data length. Implementation typically involves computing lagged products and applying FFT algorithms for efficient spectral analysis.
GUI Programming for Signal and Image Processing in MATLAB
Implementation of MATLAB Graphical User Interface (GUI) programming for 1) signal generation, basic signal operations, and Fourier transforms; 2) image file reading/display, image enhancement, edge detection, and mathematical image transformations.
Digital Watermark Embedding Technology Based on Fourier Transform
Fourier Transform-Based Digital Watermark Embedding Technology (Graduation Project with Algorithm Implementation Details)
Image Homomorphic Filtering Using Fourier Transform
Utilizing Fourier transform-based image homomorphic filtering to address uneven illumination problems in digital image processing