A quantum segmentation algorithm based on local adaptive threshold for NEQR image
This work addresses the computational bottleneck in image segmentation for large datasets, offering a quantum solution that is incremental as it adapts a classical method to quantum computing.
The authors tackled the real-time segmentation problem for large-scale images with uneven illumination by proposing a quantum segmentation algorithm based on local adaptive threshold for NEQR images, achieving an exponential speedup with complexity reduced to O(n^2+q) compared to classical methods.
The classical image segmentation algorithm based on local adaptive threshold can effectively segment images with uneven illumination, but with the increase of the image data, the real-time problem gradually emerges. In this paper, a quantum segmentation algorithm based on local adaptive threshold for NEQR image is proposed, which can use quantum mechanism to simultaneously compute local thresholds for all pixels in a gray-scale image and quickly segment the image into a binary image. In addition, several quantum circuit units, including median calculation, quantum binarization, etc. are designed in detail, and then a complete quantum circuit is designed to segment NEQR images by using fewer qubits and quantum gates. For a $2^n\times 2^n$ image with q gray-scale levels, the complexity of our algorithm can be reduced to $O(n^2+q)$, which is an exponential speedup compared to the classic counterparts. Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.