Adaptively Hashing 3DLUTs for Lightweight Real-time Image Enhancement

Fengyi Zhang, Lin Zhang, Tianjun Zhang, Dongqing Wang

Abstract: Image enhancement is an essential and longstanding task in computer vision, in which the 3D lookup table (3DLUT) is widely used due to its powerful mapping capability and high time efficiency. However, the 3DLUT generally requires a large parameter amount since it caches the mapping results for all colors of the entire discrete color space with a 3D array. As a result, standard 3DLUT-based enhancement methods suffer from heavy memory footprints, limiting their practical applications. Based on the analyses of the inherent low grid utilization rate of 3DLUT, we propose HashLUT, an efficient hash form of the standard 3DLUT, and further build a lightweight real-time image enhancement network that adaptively learns HashLUTs and handles hash collisions end-to-end. Experiments on two benchmarks demonstrate that our model achieves comparable enhancement performance to the state-of-the-art methods with significantly fewer parameters. Source codes are available at https://github.com/Xian-Bei.