MATLAB Framework for HDR Image Analysis

MATLAB-Based High Dynamic Range (HDR) Image Processing and AnalysisProject OverviewThis project focuses on developing an HDR image processing framework in MATLAB, integrating concepts from electrical engineering, data science, image processing, and statistics. The goal is to merge multiple exposure images into a single HDR image while applying contrast enhancement, tone mapping, and statistical analysis.Key Features✅ Multi-Exposure Image Fusion: Combine low-exposure and high-exposure grayscale images to enhance details in shadows and highlights.✅ Statistical Analysis of Image Quality: Use MEF-SSIM, PSNR, and entropy to evaluate HDR quality.✅ Machine Learning for Enhancement: Train a simple neural network in MATLAB for optimizing HDR tone mapping.✅ Electrical Engineering Application: Simulate sensor-based dual-gain image acquisition and analyze signal processing methods.✅ Business & Case Study Perspective: Discuss real-world applications of HDR in industries like medical imaging, surveillance, and automotive vision.Project Implementation1.Image Acquisition & Preprocessing:oLoad grayscale images with different exposures using MATLAB.oApply image registration to align images before fusion.2.HDR Image Construction:oImplement weighted fusion using exposure maps and gradient-based techniques.oApply multi-scale processing to enhance fine details.3.Tone Mapping for Display:oUse Reinhard’s or Drago’s tone mapping to convert HDR to LDR.oEnhance contrast and sharpness with adaptive gamma correction.4.Evaluation & Analysis:
oCompute MEF-SSIM, PSNR, and entropy to compare fusion quality.oApply statistical tests to validate image enhancement techniques.5.Machine Learning Enhancement (Optional):oTrain a simple neural network or CNN in MATLAB for intelligent exposure fusion.oUse data science techniques to optimize weighting functions.6.Case Study & Business Insights:oDiscuss HDR applications in surveillance, medical imaging, and photography.oCompare traditional HDR techniques vs. AI-based fusion methods.Tools & TechnologiesMATLAB (Image Processing Toolbox, Deep Learning Toolbox)Statistics & Machine Learning Toolbox for Data AnalysisElectrical Circuit Simulations (Optional) for Sensor ModelingExpected OutcomeAn optimized MATLAB-based HDR framework that enhances image quality while preserving details.Insights into AI-driven HDR processing for real-world applications.A comprehensive analysis of statistical performance metrics in HDR imaging.