Quantum Image Segmentation Based on Grayscale Morphology
This work addresses the computational bottleneck in image segmentation for applications handling large datasets, though it is incremental as it adapts classical morphology to quantum computing.
The authors tackled the real-time processing problem for large-scale grayscale image segmentation by proposing a quantum algorithm that performs morphological operations on all pixels simultaneously, achieving an exponential speedup with complexity O(n^2+q) compared to classical methods.
The classical image segmentation algorithm based on grayscale morphology can effectively segment images with uneven illumination, but with the increase of the image data, the real-time problem will emerge. In order to solve this problem, a quantum image segmentation algorithm is proposed in this paper, which can use quantum mechanism to simultaneously perform morphological operations on all pixels in a grayscale image, and then quickly segment the image into a binary image. In addition, several quantum circuit units, including dilation, erosion, bottom hat transformation, top hat transformation, etc., are designed in detail, and then they are combined together to construct the complete quantum circuits for segmenting the NEQR images. For a 2^n * 2^n image with q grayscale levels, the complexity of our algorithm can be reduced to O(n^2+q), which is an exponential speedup than 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.