Searching for Fast Demosaicking Algorithms

Karima Ma, MICHAEL GHARBI, Andrew Adams, Shoaib Kamil, Tzu-Mao Li, Connelly Barnes, Jonathan Ragan-Kelley

Abstract:

We present a method to automatically synthesize efficient, high-quality demosaicking algorithms, across a range of computational budgets, given a loss
function and training data. It performs a multi-objective, discrete-continuous
optimization which simultaneously solves for the program structure and parameters that best trade off computational cost and image quality. We design
the method to exploit domain-specific structure for search efficiency. We apply it to several tasks, including demosaicking both Bayer and Fuji X-Trans
color filter patterns, as well as joint demosaicking and super-resolution. In a
few days on 8 GPUs, it produces a family of algorithms that significantly
improves image quality relative to the prior state-of-the-art across a range of
computational budgets from 10s to 1000s of operations per pixel (1dB–3dB
higher quality at the same cost, or 8.5–200× higher throughput at same or
better quality). The resulting programs combine features of both classical
and deep learning-based demosaicking algorithms into more efficient hybrid
combinations, which are bandwidth-efficient and vectorizable by construction. Finally, our method automatically schedules and compiles all generated
programs into optimized SIMD code for modern processors.