Explore MATLAB source code curated for "噪声" with clean implementations, documentation, and examples.
This program adds multiple types of noise to images, including Gaussian noise, salt-and-pepper noise, additive and multiplicative noise, as well as various mixed noise combinations, for testing other image processing applications.
Median filtering is an effective digital image processing technique for eliminating noise from images. Our implementation allows you to run the code on your images for further processing. You can apply median filters with various kernel sizes including 3x3, 5x5, 7x7, and 9x9 - larger kernel sizes typically yield better noise reduction results by considering broader pixel neighborhoods.
PSK modulation and demodulation with noise addition - a communication principles course project focusing on digital signal processing implementation.
Image denoising implementation in MATLAB using mathematical morphology with enhanced structural element selection. Unlike traditional single-structure-element approaches that yield suboptimal results, this method utilizes multiple structuring elements (disk, square, line) through dilation and erosion operations. The implementation effectively handles Gaussian, salt-and-pepper, and Wiener noise types while maintaining algorithmic simplicity through morphological opening/closing operations with imopen() and imclose() functions.
Applying Hopfield neural networks to perform association and recognition on noisy digital image patterns "1, 2, 3, 4" to eliminate noise interference, with implementation insights on pattern storage and energy minimization algorithms.
1. MATLAB programming for implementing function reverse sorting using array manipulation techniques; 2. Program implementation of FFT and Inverse Fourier Transform with signal processing algorithms; 3. An image processing application featuring noise addition and denoising filters.
Performing Gaussian background analysis on image sequences to remove background and noise, with reference to the paper "Automatic Temporal Segmentation for Content-Based Video Coding." Includes implementation details for background modeling and foreground extraction.
Implementation of blurred image restoration using regularization techniques, generating an image with subsequent noise addition and image recovery through specific regularization parameter inputs.
Implementation of GPS parallel code acquisition algorithm utilizing 1024-point FFT processing, designed to handle input data with various noise power levels.
Morphological filtering using dilation and closing operations effectively eliminates noise and fills holes in images, with MATLAB code implementation examples.