# Low-Cost Implementation of Bilinear and Bicubic Image Interpolation for Real-Time Image Super-Resolution

arXiv: https://arxiv.org/abs/2009.09622 · HTML: https://mlsanity.com/paper/2009.09622/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution](https://mlsanity.com/paper/2303.13933/) (2023)
- [SFE-Net: EEG-based Emotion Recognition with Symmetrical Spatial Feature Extraction](https://mlsanity.com/paper/2104.06308/) (2021)
- [WATUNet: A Deep Neural Network for Segmentation of Volumetric Sweep Imaging Ultrasound](https://mlsanity.com/paper/2311.10857/) (2023)
- [Siamese-Driven Optimization for Low-Resolution Image Latent Embedding in Image Captioning](https://mlsanity.com/paper/2512.08873/) (2025)
- [Learning-Based and Quality Preserving Super-Resolution of Noisy Images](https://mlsanity.com/paper/2311.02254/) (2023)
- [Image Super-Resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features](https://mlsanity.com/paper/2401.00241/) (2023)
- [VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systems](https://mlsanity.com/paper/2507.05146/) (2025)
- [High-Resolution Magnetic Particle Imaging System Matrix Recovery Using a Vision Transformer with Residual Feature Network](https://mlsanity.com/paper/2511.02212/) (2025)
- [Again-Pose: Anchor-Guided Adaptive Inter-Frame Motion Cues Propagating for High-quality Human Pose Reconstruction](https://mlsanity.com/paper/2606.29230/) (2026)
- [Perceptually Regularized Diffusion Model for Image Super-Resolution](https://mlsanity.com/paper/2609.02016/) (2026)
- [MIAR: Medical Image Super-Resolution With Autoregressive Modeling](https://mlsanity.com/paper/2609.26103/) (2026)

## Abstract (arXiv)

Super-resolution imaging (S.R.) is a series of techniques that enhance the resolution of an imaging system, especially in surveillance cameras where simplicity and low cost are of great importance. S.R. image reconstruction can be viewed as a three-stage process: image interpolation, image registration, and fusion. Image interpolation is one of the most critical steps in the S.R. algorithms and has a significant influence on the quality of the output image. In this paper, two hardware-efficient interpolation methods are proposed for these platforms, mainly for the mobile application. Experiments and results on the synthetic and real image sequences clearly validate the performance of the proposed scheme. They indicate that the proposed approach is practically applicable to real-world applications. The algorithms are implemented in a Field Programmable Gate Array (FPGA) device using a pipelined architecture. The implementation results show the advantages of the proposed methods regarding area, performance, and output quality.