  Image processing is popular in our daily life because of the need to extract
essential information from our 3D world, including a variety of applications in
widely separated fields like bio-medicine, economics, entertainment, and
industry. The nature of visual information, algorithm complexity, and the
representation of 3D scenes in 2D spaces are all popular research topics. In
particular, the rapidly increasing volume of image data as well as increasingly
challenging computational tasks have become important driving forces for
further improving the efficiency of image processing and analysis. Since the
concept of quantum computing was proposed by Feynman in 1982, many achievements
have shown that quantum computing has dramatically improved computational
efficiency [1]. Quantum information processing exploit quantum mechanical
properties, such as quantum superposition, entanglement and parallelism, and
effectively accelerate many classical problems like factoring large numbers,
searching an unsorted database, Boson sampling, quantum simulation, solving
linear systems of equations, and machine learning. These unique quantum
properties may also be used to speed up signal and data processing. In quantum
image processing, quantum image representation plays a key role, which
substantively determines the kinds of processing tasks and how well they can be
performed.
