Lightweight and Fast Real-time Image Enhancement via Decomposition of the Spatial-aware Lookup Tables

TL;DR

This paper introduces a lightweight, real-time image enhancement method that decomposes 3D lookup tables into low-dimensional components using SVD, reducing parameters and runtime while preserving spatial awareness.

Contribution

It proposes a novel decomposition framework for 3D LUTs using SVD, enabling efficient, spatial-aware image enhancement with fewer parameters and faster processing.

Findings

Reduces model size and runtime significantly.

Maintains spatial awareness and enhancement quality.

Outperforms existing methods in efficiency and performance.

Abstract

The image enhancement methods based on 3D lookup tables (3D LUTs) efficiently reduce both model size and runtime by interpolating pre-calculated values at the vertices. However, the 3D LUT methods have a limitation due to their lack of spatial information, as they convert color values on a point-by-point basis. Although spatial-aware 3D LUT methods address this limitation, they introduce additional modules that require a substantial number of parameters, leading to increased runtime as image resolution increases. To address this issue, we propose a method for generating image-adaptive LUTs by focusing on the redundant parts of the tables. Our efficient framework decomposes a 3D LUT into a linear sum of low-dimensional LUTs and employs singular value decomposition (SVD). Furthermore, we enhance the modules for spatial feature fusion to be more cache-efficient. Extensive experimental results demonstrate that our model effectively decreases both the number of parameters and runtime while maintaining spatial awareness and performance.