Computational photography combines multiple exposures, image processing algorithms, and AI-driven models to produce photos that exceed the raw sensor limits. On Pixel-class devices, the system captures several frames, aligns them, and applies tone mapping, noise reduction, and super-resolution techniques. How does this multi-frame pipeline work, and what role does the dedicated neural processor play in real-time enhancements?
How does computational photography enhance camera performance on Pixel devices?
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Wow, in this multi-frame processing, what algorithm is used for aligning the frames, and how much latency does the dedicated neural processor add? Bro, especially in low light, how does super-resolution get activated?
When using it in practice, I capture multiple raw frames via the Camera2 API, then perform frame alignment and noise reduction on the GPU, and finally hand them over to Pixel's dedicated Neural Processing Unit (via NNAPI) for super-resolution and tone mapping. This approach ensures real-time processing while enhancing detail. I recommend enabling the `enable_neural_processor` configuration to offload compute tasks to the neural processor, which significantly reduces latency.