Searching for Fast Demosaicking Algorithms
Searching for Fast Demosaicking Algorithms
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DOI：
10.1145/3508461
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Karima Ma;Michaël Gharbi;Andrew Adams;Shoaib Kamil;Tzu-Mao Li;Connelly Barnes;Jonathan Ragan-Kelley
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文献类型：
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作者：
Karima Ma;Michaël Gharbi;Andrew Adams;Shoaib Kamil;Tzu-Mao Li;Connelly Barnes;Jonathan Ragan-Kelley
我们提出了一种在一系列计算预算中自动合成高质量的演示算法，给定损失功能和培训数据。任务，包括将拜耳和富士X-Trans颜色滤清器模式以及连接的演示和超分辨率在几天内进行8 gpus。同一吞吐量或更高的质量）结合了经典和深度学习的算法的功能，将算法分解为更有效的混合组合，这些组合是带宽有效的，并且可以通过构造来自动安排所有生成的程序。
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.