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Advanced image processing techniques including HDR imaging, tone mapping, image matting, and neural style transfer that extend the capabilities of traditional photography.
Computational photography goes beyond what traditional cameras can capture. By combining multiple images or applying sophisticated algorithms, we can overcome physical limitations of camera sensors.
What is this chapter about? We explore techniques that enhance and extend traditional photography: capturing the full dynamic range of scenes, separating foreground from background, and even transferring artistic styles between images.
Why does this matter? These techniques power smartphone photography:
How the topics connect: We start with HDR imaging—capturing scenes brighter than sensors can handle. Tone mapping compresses this range for display. Image matting separates foreground from background. Finally, neural style transfer shows the creative potential of deep learning.
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Capturing full dynamic range — multi-exposure fusion, camera response recovery, and weighting functions.
Compressing HDR to displayable range — Reinhard operators, white point control, and log-average luminance.
HDR pipeline: acquisition to display
Separating foreground from background — compositing equation, color line model, and Laplacian matting.
Artistic image transformation — content loss, Gram matrix style loss, and optimization-based synthesis.
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