Exposure Fusion

Implement exposure fusion to blend multiple exposures without HDR tonemapping, leveraging quality metrics to produce a seamless image. This technique is crucial in HDR Imaging as it allows for the combination of differently exposed images, capturing a wider dynamic range.

Exposure fusion directly blends images using weight maps based on contrast, saturation, and well-exposedness, which are calculated as $C = |\nabla I|$ , $S = \text{std}(R, G, B)$ , and $E = \exp(-\frac{(I - 0.5)^2}{2\sigma^2})$ .

Calculate contrast using the Laplacian filter.
Compute saturation as the standard deviation across color channels.
Determine well-exposedness using a Gaussian function centered at 0.5.

W = C^{w_c} \cdot S^{w_s} \cdot E^{w_e}

This technique is widely used in digital photography to capture high-contrast scenes.

Example:

Input:

images = [dark_exposure, medium_exposure, bright_exposure]

Output:

Fused image with details from all exposures

Reasoning:

Compute weight maps for each exposure
Normalize weights to sum to 1
Blend images using weights
Multi-scale blending for better results

Constraints:

images: List of differently exposed images
Return: Fused image

Example:

Constraints:

Test Results