Searching for Fast Demosaicking Algorithms
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 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 13, 2022
- Source ID
- 10.1145/3508461
Entities
People
- Andrew Adams
- Connelly Barnes
- Jonathan Ragan-Kelley
- Karima Ma
- Michael Gharbi
- Shoaib Kamil
- Tzu-mao Li
Organizations
- Adobe
- Defense Advanced Research Projects Agency
- Massachusetts Institute of Technology
- National Science Foundation
- University of California, San Diego