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

Fuente: arXiv
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Main Authors: Kim, Wontae, Lee, Keuntek, Cho, Nam Ik
Format: Preprint
Published: 2025
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author Kim, Wontae
Lee, Keuntek
Cho, Nam Ik
author_facet Kim, Wontae
Lee, Keuntek
Cho, Nam Ik
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2508_16121
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Lightweight and Fast Real-time Image Enhancement via Decomposition of the Spatial-aware Lookup Tables
Kim, Wontae
Lee, Keuntek
Cho, Nam Ik
Image and Video Processing
Computer Vision and Pattern Recognition
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.
title Lightweight and Fast Real-time Image Enhancement via Decomposition of the Spatial-aware Lookup Tables
topic Image and Video Processing
Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2508.16121