Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-Resolution

arXiv:2312.06101v2 Announce Type: replace-cross
Abstract: Conventional super-resolution (SR) schemes make heavy use of convolutional neural networks (CNNs), which involve intensive multiply-accumulate (MAC) operations, and require specialized hardware such as graphics processing units. This contradicts the regime of edge AI that often runs on devices strained by power, computing, and storage resources. Such a challenge has motivated a series of lookup table (LUT)-based SR schemes that employ simple LUT readout and largely elude CNN computation. Nonetheless, the multi-megabyte LUTs in existing …

arxiv cs.cv eess.iv image resolution tables type