Quantum Block-Matching Algorithm Using Dissimilarity Measure

Finding groups of similar image blocks within an ample search area is often necessary in different applications, such as video compression, image clustering, vector quantization, and nonlocal noise reduction. A block-matching algorithm that uses a dissimilarity measure can be applied in such scenarios. In this work, a measure that utilizes the quantum Fourier transform through the draper adder or the Swap test based on the Euclidean distance is proposed. Experiments on small representative cases with ideal and depolarizing noise simulations are implemented. In the case of the Swap test, the IBM, OQC and IonQ quantum devices have been used through the qBraid services, demonstrating potential for future near-term applications.