Taming Lookup Tables for Efficient Image Retouching
Sidi Yang Binxiao Huang Mingdeng Cao Yatai Ji Hanzhong Guo Ngai Wong Yujiu Yang
The widespread use of high-definition screens in edge devices, such as
end-user cameras, smartphones, and televisions, is spurring a significant
demand for image enhancement. Existing enhancement models often optimize for
high performance while falling short of reducing hardware inference time and
power consumption, especially on edge devices with constrained computing and
storage resources. To this end, we propose Image Color Enhancement Lookup Table
(ICELUT) that adopts LUTs for extremely efficient edge inference, without any
convolutional neural network (CNN). During training, we leverage pointwise
(1x1) convolution to extract color information, alongside a split fully
connected layer to incorporate global information. Both components are then
seamlessly converted into LUTs for hardware-agnostic deployment. ICELUT
achieves near-state-of-the-art performance and remarkably low power
consumption. We observe that the pointwise network structure exhibits robust
scalability, upkeeping the performance even with a heavily downsampled 32x32
input image. These enable ICELUT, the first-ever purely LUT-based image
enhancer, to reach an unprecedented speed of 0.4ms on GPU and 7ms on CPU, at
least one order faster than any CNN solution. Codes are available at
https://github.com/Stephen0808/ICELUT.