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.

翻译：块匹配算法用于在搜索区域内找到一组相似的图像块。相似性/不相似性度量有助于解决这一问题。在实际应用中，通常需要在大范围搜索区域内找到相似的图像块组，例如视频压缩、图像聚类、向量量化以及非局部降噪。本文采用高斯噪声和图像尺寸缩减（结合低通滤波器或域变换的拟合）进行经典图像处理。通过相位算子实现分层搜索技术对图像进行编码，并利用相位图像编码结合量子傅里叶变换与交换测试，提出了一种不相似性度量。在理想与含噪仿真条件下，以及在IBM和Ionq量子设备上通过交换测试获得了实验结果。