Lightweight and Fast Real-time Image Enhancement via Decomposition of the Spatial-aware Lookup Tables
This is an incremental improvement for real-time image enhancement applications where computational efficiency is critical.
The paper tackles the trade-off between spatial awareness and efficiency in image enhancement methods using 3D lookup tables (LUTs), proposing a decomposition approach that reduces parameters by 30% and runtime by 40% while maintaining performance.
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.