MATLAB Code Implementation for Image Sparse Representation
MATLAB programming code for image sparse representation, including graphical illustrations and implementation examples
High-Performance Image Compression Using SPIHT Algorithm
The latest MATLAB implementation of the SPIHT (Set Partitioning in Hierarchical Trees) algorithm delivers high-performance image compression through advanced wavelet transformation and efficient bit-plane coding techniques.
Dyadic Wavelet Multi-Level Decomposition
Dyadic wavelet multi-level decomposition is a wavelet-based multi-scale image decomposition method for edge detection, successfully achieving multi-scale decomposition and reconstruction of images using wavelets with key implementation algorithms and function descriptions.
ORL Face Database
The ORL Face Database is a valuable resource for face recognition tasks, containing 400 images from 40 subjects with 10 images per person - suitable for implementing recognition algorithms using techniques like PCA, LDA, or deep learning approaches.
MATLAB Code Implementation for Image Downsampling
Implementation of image downsampling using three sampling methods: nearest-neighbor interpolation, bilinear interpolation, and bicubic convolution, with detailed algorithm explanations and code implementation approaches.
Image Binarization Using Wavelet-Based Methods
Image binarization using wavelet-based approach achieves excellent results with improved detail preservation and noise reduction.
Four-Neighborhood Weighted Average Algorithm for High-Quality Image Resampling
Implementation of high-quality image resampling using the four-neighborhood weighted average algorithm with pixel interpolation techniques
MATLAB Code Implementation for Corner Detection
This corner detection program can identify all convex and concave points in images and display them on binary images with excellent results. Simply run corner.m directly - the implementation utilizes feature detection algorithms to locate key points where image intensity changes significantly in multiple directions.
Fusion Method Evaluation
Application Background: The fusion process integrates two or more distinct images using similar or different modalities. Through fusion techniques, we can consolidate all useful information from multiple sources into a single comprehensive image. Key Technology: Evaluation of fusion quality through various similarity metrics.
Two-Dimensional Grayscale Image Processing Using Haar Wavelet Transform and EZW Algorithm
For an input two-dimensional grayscale image, this process first applies the lifting Haar wavelet transform, then compresses the wavelet coefficients using the classic EZW algorithm, and finally reconstructs the original image through inverse transformation. The implementation involves key steps including wavelet decomposition, coefficient quantization, and hierarchical encoding for efficient compression.