# Learning Series-Parallel Lookup Tables for Efficient Image Super-Resolution

arXiv: https://arxiv.org/abs/2207.12987 · HTML: https://mlsanity.com/paper/2207.12987/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2022.

## Built on by (most cited first, 17 of 17)

- [Reconstructed Convolution Module Based Look-Up Tables for Efficient Image Super-Resolution](https://mlsanity.com/paper/2307.08544/) (2023)
- [Taming Lookup Tables for Efficient Image Retouching](https://mlsanity.com/paper/2403.19238/) (2024)
- [Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up Tables](https://mlsanity.com/paper/2303.14506/) (2023)
- [DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup Tables](https://mlsanity.com/paper/2503.15931/) (2025)
- [Online Streaming Video Super-Resolution with Convolutional Look-Up Table](https://mlsanity.com/paper/2303.00334/) (2023)
- [Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-Resolution](https://mlsanity.com/paper/2312.06101/) (2023)
- [Lightweight and Fast Real-time Image Enhancement via Decomposition of the Spatial-aware Lookup Tables](https://mlsanity.com/paper/2508.16121/) (2025)
- [Learning Dual-Level Deformable Implicit Representation for Real-World Scale Arbitrary Super-Resolution](https://mlsanity.com/paper/2403.10925/) (2024)
- [Online Video Quality Enhancement with Spatial-Temporal Look-up Tables](https://mlsanity.com/paper/2311.13616/) (2023)
- [Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables](https://mlsanity.com/paper/2503.23793/) (2025)
- [ShiftLUT: Spatial Shift Enhanced Look-Up Tables for Efficient Image Restoration](https://mlsanity.com/paper/2603.00906/) (2026)
- [LeRF: Learning Resampling Function for Adaptive and Efficient Image Interpolation](https://mlsanity.com/paper/2407.09935/) (2024)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)
- [Receptive Field Expanded Look-Up Tables for Vision Inference: Advancing from Low-level to High-level Tasks](https://mlsanity.com/paper/2510.10522/) (2025)
- [Anisotropic Pooling for LUT-realizable CNN Image Restoration](https://mlsanity.com/paper/2510.21437/) (2025)
- [PixelSR: Efficient Screen Content Super-Resolution via Pixel Classification](https://mlsanity.com/paper/2608.00646/) (2026)
- [Hybrid-LUT: Channel-Aware Hybrid Lookup Table and Filtering for Efficient Image Denoising](https://mlsanity.com/paper/2608.11646/) (2026)

## Abstract (arXiv)

Lookup table (LUT) has shown its efficacy in low-level vision tasks due to the valuable characteristics of low computational cost and hardware independence. However, recent attempts to address the problem of single image super-resolution (SISR) with lookup tables are highly constrained by the small receptive field size. Besides, their frameworks of single-layer lookup tables limit the extension and generalization capacities of the model. In this paper, we propose a framework of series-parallel lookup tables (SPLUT) to alleviate the above issues and achieve efficient image super-resolution. On the one hand, we cascade multiple lookup tables to enlarge the receptive field of each extracted feature vector. On the other hand, we propose a parallel network which includes two branches of cascaded lookup tables which process different components of the input low-resolution images. By doing so, the two branches collaborate with each other and compensate for the precision loss of discretizing input pixels when establishing lookup tables. Compared to previous lookup table-based methods, our framework has stronger representation abilities with more flexible architectures. Furthermore, we no longer need interpolation methods which introduce redundant computations so that our method can achieve faster inference speed. Extensive experimental results on five popular benchmark datasets show that our method obtains superior SISR performance in a more efficient way. The code is available at https://github.com/zhjy2016/SPLUT.