A hybrid quantum image edge detector for the NISQ era

Abstract

Edges are image locations where the gray value intensity changes suddenly. They are among the most important features to understand and segment an image. Edge detection is a standard task in digital image processing, solved, for example, using filtering techniques. However, the amount of data to be processed grows rapidly and pushes even supercomputers to their limits. Quantum computing promises exponentially lower memory usage in terms of the number of qubits compared to the number of classical bits. In this paper, we propose a hybrid method for quantum edge detection based on the idea of a quantum artificial neuron. Our method can be practically implemented on quantum computers, especially on those of the current noisy intermediate-scale quantum era. We compare six variants of the method to reduce the number of circuits and thus the time required for the quantum edge detection. Taking advantage of the scalability of our method, we can practically detect edges in images considerably larger than reached before.

Analisis Deteksi Tepi Citra Dengan Quantum Hadamard Edge Detection (QHED)

“…Quantum Hadamard Edge Detection (QHED) merupakan salah satu algoritma pendeteksian tepi dengan pendekatan komputasi kuantum menggunakan transformasi hadamard. Pada QHED memiliki skema pengkodean gambar yang efisien dalam menghemat ruang memori dengan menggunakan pengkodean amplitudo terhadap penurunan eksponensial dalam jumlah qubit yang digunakan yaitu 𝑛 = 𝑙𝑜𝑔 2 𝑁 [6]. Namun kompleksitas waktu dalam persiapan awal pada pengkodean gambar sangatlah tinggi.…”

Image Enhancement via Special Functions and Its Application for Near Infrared Imaging

“…Since the use of the traditional operators cannot have accurate profile extraction, researchers have come up with various novel ways for the image enhancement. [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] Among them, mathematical method has been intensively used for image enhancement. One good example is shown in the application of the Hausdorff derivative, which is used for image edge extraction.…”

“…[12] The other research efforts for acquiring the image enhancement involved with the method of using wavelets. [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] The wavelets are generally wave-like oscillatory functions. They show a recovery impact since the features acquired via the wavelets show different resolutions.…”