Look-Up Table Compression for Efficient Image Restoration

Look-Up Table (LUT) has recently gained increasing attention for restoring High-Quality (HQ) images from Low-Quality (LQ) observations, thanks to its high computational efficiency achieved through a "space for time" strategy of caching learned LQ-HQ pairs. However, incorporating multiple LUTs for improved performance comes at the cost of a rapidly growing storage size, which is ultimately restricted by the allocatable on-device cache size. In this work, we propose a novel LUT compression framework to achieve a better trade-off between storage size and performance for LUT-based image restoration models. Based on the observation that most cached LQ image patches are distributed along the diagonal of a LUT, we devise a Diagonal-First Compression (DFC) framework, where diagonal LQ-HQ pairs are preserved and carefully re-indexed to maintain the representation capacity, while non-diagonal pairs are aggressively subsampled to save storage. Extensive experiments on representative image restoration tasks demonstrate that our DFC framework significantly reduces the storage size of LUT-based models (including our new design) while maintaining their performance. For instance, DFC saves up to 90% of storage at a negligible performance drop for ×4 super-resolution. The source code is available on GitHub: https://github.com/leenas233/DFC .