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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 tradeoff 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 10 s to 1000 s of operations per pixel (1 dB–3 dB 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.more » « less
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Bangaru, Sai Praveen ; Gharbi, Michael ; Luan, Fujun ; Li, Tzu-Mao ; Sunkavalli, Kalyan ; Hasan, Milos ; Bi, Sai ; Xu, Zexiang ; Bernstein, Gilbert ; Durand, Fredo ( , ACM transactions on graphics)
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Hong, James ; Fisher, Matthew ; Gharbi, Michael ; Fatahalian, Kayvon ( , International Conference on Computer Vision (ICCV))
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Mehta, Ishit ; Gharbi, Michael ; Barnes, Connelly ; Shechtman, Eli ; Ramamoorthi, Ravi ; Chandraker, Manmohan ( , IEEE/CVF International Conference on Computer Vision)