CLUT-Net: Learning Adaptively Compressed Representations of 3DLUTs for Lightweight Image Enhancement

Fengyi Zhang, Hui Zeng, Tianjun Zhang, Lin Zhang

Abstract: Learning-based image enhancement has made great progress recently, among which the 3-Dimensional LookUp Table (3DLUT) based methods achieve a good balance between enhancement performance and time-efficiency. Generally, the more basis 3DLUTs are used in such methods, the more application scenarios could be covered, and thus the stronger enhancement capability could be achieved. However, more 3DLUTs would also lead to the rapid growth of the parameter amount, since a single 3DLUT has as many as D3 parameters where D is the table length. A large parameter amount not only hinders the practical application of the 3DLUT-based schemes but also gives rise to the training difficulty and does harm to the effectiveness of the basis 3DLUTs, leading to even worse performances with more utilized 3DLUTs. Through in-depth analysis of the inherent compressibility of 3DLUT, we propose an effective Compressed representation of 3-dimensional LookUp Table (CLUT) which maintains the powerful mapping capability of 3DLUT but with a significantly reduced parameter amount. Based on CLUT, we further construct a lightweight image enhancement network, namely CLUT-Net, in which image-adaptive and compression-adaptive CLUTs are learned in an end-to-end manner. Extensive experimental results on three benchmark datasets demonstrate that our proposed CLUT-Net outperforms the existing state-of-the-art image enhancement methods with orders of magnitude smaller parameter amounts. The source codes are available at https://github.com/Xian-Bei/CLUT-Net.