Adaptive Histogram Equalization: Image Enhancement and Edge Detection Techniques
Image enhancement and edge detection algorithms for improving visual quality and identifying object boundaries
Homomorphic Filtering for Image Processing
Applying homomorphic filtering to images followed by local histogram equalization can achieve blur enhancement effects, suitable for applications such as thin cloud removal in remote sensing images. However, this approach requires further refinement and research for optimal results.
Wavelet Transform-Based Image Enhancement
Wavelet Transform-Based Image Enhancement with Complete Implementation Workflow
Image Enhancement Techniques Using Fractional-Order Differentiation
Image enhancement technology based on fractional-order differentiation, where the code leverages gradient information to better enhance high-frequency components while nonlinearly preserving low-frequency regions. Implementation typically involves constructing fractional differential masks and applying convolution operations to extract multi-scale features.
Image Enhancement
2. Image Enhancement: Image enhancement, also known as image sharpening, aims to strengthen detail information such as contour edges in images. Similar to image smoothing, image enhancement can be performed in both spatial and frequency domains. For spatial domain sharpening: subtracting the original image from a processed version yields the sharpened image g(i,j), i.e., g(i,j) = f(i,j) - α[f(i+1,j) + f(i-1,j) + f(i,j+1) + f(i,j-1) - 4f(i,j)] = f(i,j) + 4α[f(i,j) - avg]. Here α is an adjustable factor controlling sharpening intensity, where 1/4 represents the average gray value of the 4 neighboring points around the sharpened pixel.
Image Segmentation Using Region Growing Method with Denoising and Enhancement
Implementation of region growing algorithm for image segmentation complemented by noise reduction techniques and image enhancement methods to improve overall visual quality
Image Enhancement Using Dyadic Wavelet Transform
This practical implementation of image enhancement via dyadic wavelet transform includes sample images and complete source code, featuring multi-scale decomposition and reconstruction algorithms for detail preservation and noise reduction.
Wavelet Transform for Image Enhancement with MATLAB Source Code
Implementation of Image Enhancement Using Wavelet Transform with MATLAB Source Code
Image Enhancement via Gaussian Low-Pass Filtering, Gradient Operations, and Laplacian Operator
Enhance image quality using Gaussian low-pass filtering, gradient operations, and Laplacian operator techniques, featuring an interactive GUI interface for parameter control and real-time visualization of processing effects.
Image Enhancement Based on Dyadic Wavelet Transform
This is a MATLAB source code implementation for image enhancement using dyadic wavelet transform, successfully compiled and functional.