Synthesizing HDR Images Using MATLAB

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Resource Overview

Our algorithm leverages multiple exposure-bracketed photographs to reconstruct the camera response function through a complementary methodology that maximizes luminance range. The derived response function enables fusion of multiple images into a single high dynamic range (HDR) image with enhanced pixel accuracy representing real-world scenes. Validation using both optical images from traditional cameras and digital image acquisition methods demonstrates the algorithm's correctness and robustness, with implementations featuring pixel-wise weighting and radiance map reconstruction techniques.

Detailed Documentation

This paper presents an algorithm that utilizes multiple exposure-bracketed photographs to generate the camera response function. Through a complementary approach implemented via weighted least-squares optimization, we extend the luminance range to its maximum capacity. The recovered response function enables the algorithm to merge multiple images into a high dynamic range image that more accurately represents real-world scene radiance, incorporating techniques like radiance map fusion and tone mapping. The algorithm's validity has been verified through both optical images from conventional cameras and digital image acquisition methods, with experimental results demonstrating reliable HDR synthesis through histogram analysis and quantitative error metrics. These validation procedures confirm the algorithm's reliability and effectiveness in practical applications.

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