MATLAB Implementation of Dark Channel Prior Image Enhancement
Complete MATLAB implementation of dark channel prior image enhancement algorithm with full source code, based on the original research by the algorithm's proposer.
MATLAB Implementation of High-Pass Gaussian Filtering with LBP and LDP
MATLAB source codes for High-Pass Gaussian Filtering, Local Binary Patterns (LBP), and Local Derivative Patterns (LDP) for image enhancement and feature extraction applications.
Image Enhancement Using Diffusion Filtering
Implementation of image enhancement through diffusion filtering, including Gaussian preprocessing, gradient computation, structural tensor calculation, diffusion tensor derivation, numerical discretization, and enhancement evaluation code with algorithmic explanations
Image Enhancement Using Adaptive Genetic Algorithm
This program implements image enhancement through an adaptive genetic algorithm, with core concepts derived from a research paper published in the "Chinese Journal of Computers". The implementation includes population initialization, fitness evaluation based on image quality metrics, and adaptive crossover/mutation operations.
Wavelet Transform for Image Enhancement - MATLAB Implementation with Source Code
MATLAB wavelet transform code for image enhancement containing complete source programs, wavelet decomposition/reconstruction algorithms, and practical implementation examples
Medical Image Enhancement through Advanced Image Processing Techniques
Implementation of Medical Image Enhancement Using Various Processing Methods and Algorithms
Image Segmentation Using PCNN Implementation in MATLAB
MATLAB-based PCNN implementation for image segmentation, edge detection, and image enhancement with algorithm explanations and key function descriptions
Image Enhancement: Techniques to Improve Picture Quality
The objective of image enhancement is to improve picture quality by increasing contrast, reducing blur and noise, and correcting geometric distortions, while image restoration is a technique that estimates the original image assuming known models of blur or noise. Image enhancement methods are categorized into frequency domain and spatial domain approaches. Frequency domain methods treat images as 2D signals and employ 2D Fourier transform for signal enhancement, with low-pass filtering to remove noise and high-pass filtering to sharpen edges. Spatial domain algorithms include local averaging and median filtering (using the median pixel value in a local neighborhood) for noise reduction.
MATLAB Implementation of Wavelet Decomposition and Reconstruction
Implementation of image enhancement and fusion using wavelet decomposition and reconstruction techniques, featuring flexible wavelet selection and beginner-friendly code structure with detailed algorithm explanations.
Retinex Algorithm Using Center-Surround Function
A Retinex algorithm implementation employing center-surround functions for effective enhancement of low-light vision images, featuring Gaussian convolution operations and logarithmic domain processing