Enhanced Image Enhancement Algorithm Integrating Laplacian Pyramid and Unsharp Masking Techniques
MATLAB-based image enhancement algorithm combining Laplacian Pyramid decomposition with improved unsharp masking methodology
Retinex-Based Image Enhancement Algorithm with Multi-Scale Implementation
Implementation of multi-scale MSR (Multi-Scale Retinex) image enhancement algorithm based on Retinex theory, validated with experimental results
Image Enhancement Algorithm Combining Common Grayscale Transformation and Improved Histogram Equalization Techniques
This study proposes a contrast enhancement algorithm based on conventional grayscale transformation and histogram equalization methods. The algorithm implements a multi-stage approach involving histogram smoothing, histogram equalization, uniform grayscale distribution across the display range, and median filtering for noise reduction.
Retinex Image Enhancement Algorithm SSR
The Retinex algorithm enables adaptive enhancement for various image types, offering superior adaptability compared to traditional single-method enhancement approaches. While conventional algorithms typically enhance only specific image features, Retinex achieves optimal balance in dynamic range compression, detail enhancement, and color correction through its multi-scale processing approach using Gaussian surround functions. Primarily applied in underwater image restoration, the algorithm's core implementation involves separating illumination and reflectance components through logarithmic operations and spatial filtering.
MSRCR Image Enhancement Algorithm Based on Retinex Theory
Source code implementation of Retinex-based MSRCR image enhancement algorithm with tested functionality and ready-to-use deployment