# Training-free Quantum-Inspired Image Edge Extraction Method

arXiv: https://arxiv.org/abs/2501.18929 · HTML: https://mlsanity.com/paper/2501.18929/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 79 references that are in the map.
- First posted on arXiv 31 Jan 2025.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Pixel Difference Networks for Efficient Edge Detection](https://mlsanity.com/paper/2108.07009/) (2021)
- [Richer Convolutional Features for Edge Detection](https://mlsanity.com/paper/1612.02103/) (2016)
- [Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection](https://mlsanity.com/paper/1909.01955/) (2019)
- [Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation](https://mlsanity.com/paper/1503.00848/) (2015)
- [Learning to predict crisp boundaries](https://mlsanity.com/paper/1807.10097/) (2018)
- [Bi-Directional Cascade Network for Perceptual Edge Detection](https://mlsanity.com/paper/1902.10903/) (2019)
- [Photo-Sketching: Inferring Contour Drawings from Images](https://mlsanity.com/paper/1901.00542/) (2019)
- [Fast Edge Detection Using Structured Forests](https://mlsanity.com/paper/1406.5549/) (2014)
- [EDTER: Edge Detection with Transformer](https://mlsanity.com/paper/2203.08566/) (2022)
- [Object Contour Detection with a Fully Convolutional Encoder-Decoder Network](https://mlsanity.com/paper/1603.04530/) (2016)
- [RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth](https://mlsanity.com/paper/2108.00616/) (2021)
- [Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction](https://mlsanity.com/paper/1801.00524/) (2018)
- [Physics-Informed Computer Vision: A Review and Perspectives](https://mlsanity.com/paper/2305.18035/) (2023)
- [Oriented Edge Forests for Boundary Detection](https://mlsanity.com/paper/1412.4181/) (2014)
- [ N4 -Fields: Neural Network Nearest Neighbor Fields for Image Transforms](https://mlsanity.com/paper/1406.6558/) (2014)
- [Object Contour and Edge Detection with RefineContourNet](https://mlsanity.com/paper/1904.13353/) (2019)
- [Quantum-Inspired Machine Learning: a Survey](https://mlsanity.com/paper/2308.11269/) (2023)
- [Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection](https://mlsanity.com/paper/1608.08029/) (2016)
- [SemiContour: A Semi-supervised Learning Approach for Contour Detection](https://mlsanity.com/paper/1605.04996/) (2016)
- [FINED: Fast Inference Network for Edge Detection](https://mlsanity.com/paper/2012.08392/) (2020)
- [Pixel-wise Deep Learning for Contour Detection](https://mlsanity.com/paper/1504.01989/) (2015)
- [A hybrid quantum image edge detector for the NISQ era](https://mlsanity.com/paper/2203.12072/) (2022)
- [Quantum Edge Detection for Image Segmentation in Optical Environments](https://mlsanity.com/paper/1409.2918/) (2014)

## Abstract (arXiv)

Edge detection is a cornerstone of image processing, yet existing methods often face critical limitations. Traditional deep learning edge detection methods require extensive training datasets and fine-tuning, while classical techniques often fail in complex or noisy scenarios, limiting their real-world applicability. To address these limitations, we propose a training-free, quantum-inspired edge detection model. Our approach integrates classical Sobel edge detection, the Schr"odinger wave equation refinement, and a hybrid framework combining Canny and Laplacian operators. By eliminating the need for training, the model is lightweight and adaptable to diverse applications. The Schr"odinger wave equation refines gradient-based edge maps through iterative diffusion, significantly enhancing edge precision. The hybrid framework further strengthens the model by synergistically combining local and global features, ensuring robustness even under challenging conditions. Extensive evaluations on datasets like BIPED, Multicue, and NYUD demonstrate superior performance of the proposed model, achieving state-of-the-art metrics, including ODS, OIS, AP, and F-measure. Noise robustness experiments highlight its reliability, showcasing its practicality for real-world scenarios. Due to its versatile and adaptable nature, our model is well-suited for applications such as medical imaging, autonomous systems, and environmental monitoring, setting a new benchmark for edge detection.