Research On Quantum Image Segmentation Algorithm Based On NEQR Mode

Image segmentation algorithm can segment an image into several non-overlapping parts to find the objects,but with the increase of image data,the real-time problem gradually appears.Quantum computing can use quantum mechanism to accelerate classical image segmentation algorithm at the highest exponential level.However,the existing quantum segmentation algorithms use more quantum resources(qubit and quantum gate),and cannot process images containing multiple objects.In addition,the existing quantum algorithms cannot segment common images containing uneven illumination.Based on these problems,the complex quantum image(including images with multiple targets and uneven illumination)segmentation are deeply explored in this paper.The main works are as follows.(1)To address the problem that the existing dual-threshold quantum image segmentation algorithm cannot segment images containing multiple objects,an improved quantum image segmentation algorithm based on double thresholds is proposed,which can use less quantum resources to segment complex gray-scale images into clear ternary images,and can be extended to n+1 times segmentation by using n thresholds.In addition,a feasible quantum comparator is designed to compare the relationship between threshold and the gray-scale value of pixels,and then a scalable quantum circuit is designed to segment novel enhanced quantum representation images.For a2~n×2 ~nimage 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.(2)For the problem that existing algorithms cannot segment images with uneven illumination,based on the classical local adaptive threshold method,a quantum image segmentation algorithm based on local adaptive threshold is proposed,which can use quantum mechanism to simultaneously calculate the local threshold of all pixels in a gray-scale image,and quickly segment the image into a binary image.In addition,several quantum circuit units are designed,including median calculation,quantum binarization,etc.Then based on these quantum units and fewer qubits and quantum gates,a complete quantum circuit is designed.For a2~n×2 ~nimage with q gray-scale levels,the complexity of the algorithm can be reduced toO(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 era.(3)For the problem that existing algorithms cannot segment images with uneven illumination,based on the classical grayscale morphology operation,a quantum image segmentation algorithm based on grayscale morphology is proposed,which uses quantum mechanism to perform morphological operations on all pixels in the gray-scale image at the same time,and then quickly segments the image into a binary image.In addition,several quantum circuit units,such as dilation,erosion,bottom-hat transformation and top-hat transformation,are designed in detail,and they are combined to build a complete quantum circuit.For a 2~n×2~nimage with q grayscale levels,the complexity of the algorithm can be reduced toO(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 era.