# An improved two-threshold quantum segmentation algorithm for NEQR image

arXiv: https://arxiv.org/abs/2311.12033 · HTML: https://mlsanity.com/paper/2311.12033/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 36 references that are in the map.
- First posted on arXiv 2 Oct 2023.

## Builds on (most cited first, 1 of 1)

- [Quantum image edge detection based on eight-direction Sobel operator for NEQR](https://mlsanity.com/paper/2310.03037/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Fast Quantum Image Compression Algorithm based on Taylor Expansion](https://mlsanity.com/paper/2502.10684/) (2025)

## Abstract (arXiv)

The quantum image segmentation algorithm is to divide a quantum image into several parts, but most of the existing algorithms use more quantum resource(qubit) or cannot process the complex image. In this paper, an improved two-threshold quantum segmentation algorithm for NEQR image is proposed, which can segment the complex gray-scale image into a clear ternary image by using fewer qubits and can be scaled to use n thresholds for n + 1 segmentations. In addition, a feasible quantum comparator is designed to distinguish the gray-scale values with two thresholds, and then a scalable quantum circuit is designed to segment the NEQR image. For a 2^(n)*2^(n) image with q gray-scale levels, the quantum cost of our algorithm can be reduced to 60q-6, which is lower than other existing quantum algorithms and does not increase with the image's size increases. The experiment on IBM Q demonstrates that our algorithm can effectively segment the image.