A block-matching algorithm finds a group of similar image patches inside a search area. Similarity/dissimilarity measures can help to solve this problem. In different practical applications, finding groups of similar image blocks within an ample search area is often necessary, such as video compression, image clustering, vector quantization, and nonlocal noise reduction. In this work, classical image processing is performed using Gaussian noise and image size reduction with a fit of a Low-Pass Filter or Domain Transform. A hierarchical search technique is implemented to encode the images by phase operator. Using phase image coding with the quantum Fourier transform and the Swap test, we propose a dissimilarity measure. Results were obtained with perfect and noisy simulations and in the case of the Swap test with the IBM and Ionq quantum devices.

通过使用分块匹配算法和相似度/不相似度测量，本研究在图像处理过程中，利用高斯噪声和图像大小减小的低通滤波器或域转换，并采用分阶段搜索技术通过相位操作符对图像进行编码。通过量子傅里叶变换和Swap测试对相位图像编码进行相似度度量，结果在理想和噪声模拟以及与IBM和Ionq量子设备进行Swap测试的情况下获得。

在嘈杂环境下使用差异度量的量子经典混合块匹配算法