Quantum image interest point extraction

2021 ◽  
pp. 2150063
Author(s):  
Nan Jiang ◽  
Zhuoxiao Ji ◽  
Hong Li ◽  
Jian Wang
Keyword(s):  
Image Processing ◽  
Time Complexity ◽  
Luminance Contrast ◽  
Target Object ◽  
Interest Point ◽  
Interest Points ◽  
Quantum Image ◽  
Absolute Value ◽  
Classical Image ◽  
Better Than

With the development of quantum computing, the application of it to image processing has lots of advantages compared to classical image processing. In this paper, we propose a scheme to extract the interest point in quantum images. Interest point is a kind of feature point which can help to identify the target object in the image. Our scheme is based on the idea of Luminance Contrast (LC) algorithm. The scheme computes the absolute value of gray level differences between a pixel and the others, and then adds all these differences together. The sum is defined as a saliency. After computing the saliency of every pixel, we label the pixels with the maximal saliency as the interest points. The algorithm has pretty good performance and its time complexity is much better than the classical algorithm in same conditions, which provides a new idea for the extraction of image interest point.

Quantum generative adversarial networks for learning and loading quantum image in noisy environment

2021 ◽  
pp. 2150360
Author(s):  
Wanghao Ren ◽  
Zhiming Li ◽  
Yiming Huang ◽  
Runqiu Guo ◽  
Lansheng Feng ◽  
...  
Keyword(s):  
Image Processing ◽  
Quantum Circuit ◽  
Quantum Image Processing ◽  
Quantum Circuits ◽  
Generative Adversarial Networks ◽  
Channel Noise ◽  
Quantum Image ◽  
Adversarial Networks ◽  
Potential Applications ◽  
Classical Image

Quantum machine learning is expected to be one of the potential applications that can be realized in the near future. Finding potential applications for it has become one of the hot topics in the quantum computing community. With the increase of digital image processing, researchers try to use quantum image processing instead of classical image processing to improve the ability of image processing. Inspired by previous studies on the adversarial quantum circuit learning, we introduce a quantum generative adversarial framework for loading and learning a quantum image. In this paper, we extend quantum generative adversarial networks to the quantum image processing field and show how to learning and loading an classical image using quantum circuits. By reducing quantum gates without gradient changes, we reduced the number of basic quantum building block from 15 to 13. Our framework effectively generates pure state subject to bit flip, bit phase flip, phase flip, and depolarizing channel noise. We numerically simulate the loading and learning of classical images on the MINST database and CIFAR-10 database. In the quantum image processing field, our framework can be used to learn a quantum image as a subroutine of other quantum circuits. Through numerical simulation, our method can still quickly converge under the influence of a variety of noises.

Medical Data Are Safe

2019 ◽  
pp. 142-165
Author(s):  
Padmapriya Praveenkumar ◽  
Santhiyadevi R. ◽  
Amirtharajan R.
Keyword(s):  
Image Processing ◽  
Hash Function ◽  
Medical Data ◽  
Quantum Image Processing ◽  
Second Step ◽  
Encryption Scheme ◽  
Quantum Image ◽  
Image Communication ◽  
Medical Diagnostic ◽  
Classical Image

In this internet era, transferring and preservation of medical diagnostic reports and images across the globe have become inevitable for the collaborative tele-diagnosis and tele-surgery. Consequently, it is of prime importance to protect it from unauthorized users and to confirm integrity and privacy of the user. Quantum image processing (QIP) paves a way by integrating security algorithms in protecting and safeguarding medical images. This chapter proposes a quantum-assisted encryption scheme by making use of quantum gates, chaotic maps, and hash function to provide reversibility, ergodicity, and integrity, respectively. The first step in any quantum-related image communication is the representation of the classical image into quantum. It has been carried out using novel enhanced quantum representation (NEQR) format, where it uses two entangled qubit sequences to hoard the location and its pixel values of an image. The second step is performing transformations like confusion, diffusion, and permutation to provide an uncorrelated encrypted image.

Image Representation, Filtering, and Natural Computing in a Multivalued Quantum System

2016 ◽  
pp. 28-56 ◽  
Author(s):  
Sanjay Chakraborty ◽  
Lopamudra Dey
Keyword(s):  
Image Processing ◽  
Quantum System ◽  
Quantum Physics ◽  
Image Representation ◽  
Quantum Image Processing ◽  
Natural Computing ◽  
Quantum Image ◽  
Image Pixels ◽  
Classical Image ◽  
And Storage

Image processing on quantum platform is a hot topic for researchers now a day. Inspired from the idea of quantum physics, researchers are trying to shift their focus from classical image processing towards quantum image processing. Storing and representation of images in a binary and ternary quantum system is always one of the major issues in quantum image processing. This chapter mainly deals with several issues regarding various types of image representation and storage techniques in a binary as well as ternary quantum system. How image pixels can be organized and retrieved based on their positions and intensity values in 2-states and 3-states quantum systems is explained here in detail. Beside that it also deals with the topic that focuses on the clear filteration of images in quantum system to remove unwanted noises. This chapter also deals with those important applications (like Quantum image compression, Quantum edge detection, Quantum Histogram etc.) where quantum image processing associated with some of the natural computing techniques (like AI, ANN, ACO, etc.).

Medical Data Are Safe

2021 ◽  
pp. 302-324
Author(s):  
Padmapriya Praveenkumar ◽  
Santhiyadevi R. ◽  
Amirtharajan R.
Keyword(s):  
Image Processing ◽  
Hash Function ◽  
Medical Data ◽  
Quantum Image Processing ◽  
Second Step ◽  
Encryption Scheme ◽  
Quantum Image ◽  
Image Communication ◽  
Medical Diagnostic ◽  
Classical Image

In this internet era, transferring and preservation of medical diagnostic reports and images across the globe have become inevitable for the collaborative tele-diagnosis and tele-surgery. Consequently, it is of prime importance to protect it from unauthorized users and to confirm integrity and privacy of the user. Quantum image processing (QIP) paves a way by integrating security algorithms in protecting and safeguarding medical images. This chapter proposes a quantum-assisted encryption scheme by making use of quantum gates, chaotic maps, and hash function to provide reversibility, ergodicity, and integrity, respectively. The first step in any quantum-related image communication is the representation of the classical image into quantum. It has been carried out using novel enhanced quantum representation (NEQR) format, where it uses two entangled qubit sequences to hoard the location and its pixel values of an image. The second step is performing transformations like confusion, diffusion, and permutation to provide an uncorrelated encrypted image.

Image Processing Using Quantum Computing

2021 ◽  
pp. 186-201
Author(s):  
Bably Dolly ◽  
Deepa Raj
Keyword(s):  
Image Processing ◽  
Quantum Computing ◽  
Image Representation ◽  
Research Papers ◽  
Quantum Image ◽  
Current Scenario ◽  
Pros And Cons ◽  
Recent Trends ◽  
Classical Image ◽  
Quantum Image Representation

Image processing via the quantum platform is an emerging area for researchers. Researchers are more interested to move on towards quantum image processing instead of classical image processing. This chapter starts with the review of different quantum image computing-based research papers with a brief idea of the ethics which inspire quantum computing in the background and focus on the current scenario of recent trends of quantum image representation, pitfalls, and summarization of the pros and cons of it, with the limitations of the technologies used and focus on the recent work to be going on and application of it in a different field. In the next, it will focus on the different methods used by the researcher in the previous papers. The next section discussed the different methods based on quantum image representation used. Some different techniques of image storage, retrieval, and representation in a quantum system are discussed. Also, this chapter briefs the pros and cons of using different techniques in quantum systems in comparison to classical systems.

A New Kind of Based on the Graph K-Means Clustering Initial Center Selection Algorithm

2012 ◽  
Vol 241-244 ◽  
pp. 2845-2848 ◽  
Author(s):  
Hai Yan Zhou
Keyword(s):  
Image Processing ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Simple Algorithm ◽  
Geometric Meaning ◽  
Direct Impact ◽  
Selection Algorithm ◽  
Initial Cluster ◽  
Cluster Class ◽  
Better Than

K-means clustering algorithm is simple and fast, and has more intuitive geometric meaning, which has been widely applied in pattern recognition, image processing and computer vision. It has obtained satisfactory results. But it need to determine the initial cluster class center before executing the k-means algorithm, and the choice of the initial cluster class center has a direct impact on the final clustering results. A selection algorithm is proposed, which based on figure node most magnanimous to determine the initial cluster class center of K-means clustering algorithm. The method compares with the selection algorithm of other initial cluster class center, which has a simple algorithm idea and low time complexity, and it is significantly better than other clustering arithmetic.

scholarly journals Quantum Image Processing: Opportunities and Challenges

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yue Ruan ◽  
Xiling Xue ◽  
Yuanxia Shen
Keyword(s):  
Image Processing ◽  
Quantum Mechanics ◽  
Quantum Computing ◽  
Quantum Computer ◽  
Quantum Image Processing ◽  
Quantum Image ◽  
Intermediate Scale ◽  
Classical Image ◽  
Image Format ◽  
Significant Opportunity

Quantum image processing (QIP) is a research branch of quantum information and quantum computing. It studies how to take advantage of quantum mechanics’ properties to represent images in a quantum computer and then, based on that image format, implement various image operations. Due to the quantum parallel computing derived from quantum state superposition and entanglement, QIP has natural advantages over classical image processing. But some related works misuse the notion of quantum superiority and mislead the research of QIP, which leads to a big controversy. In this paper, after describing this field’s research status, we list and analyze the doubts about QIP and argue “quantum image classification and recognition” would be the most significant opportunity to exhibit the real quantum superiority. We present the reasons for this judgment and dwell on the challenges for this opportunity in the era of NISQ (Noisy Intermediate-Scale Quantum).

Design and simulation of quantum image binarization using quantum comparator

2019 ◽  
Vol 35 (09) ◽  
pp. 2050049 ◽  
Author(s):  
Hai-Ying Xia ◽  
Han Zhang ◽  
Shu-Xiang Song ◽  
Haisheng Li ◽  
Yi-Jie Zhou ◽  
...  
Keyword(s):  
Image Processing ◽  
Quantum Gates ◽  
Basic Operation ◽  
Quantum Cost ◽  
Efficient Design ◽  
Circuit Implementation ◽  
Image Binarization ◽  
Quantum Image ◽  
Classical Image ◽  
Complete Circuit

Compared with classical image processing, quantum image processing provides a possible solution for faster image processing, which has been widely concerned. Quantum image binarization is a basic operation and plays an important role in image processing. Hence, we proposed an efficient design of quantum image binarization using quantum comparator. To reduce quantum cost and quantum delay, the comparator was optimized by rearranging the quantum gates. Then, a complete circuit implementation of quantum image binarization was designed using the comparator. Furthermore, we analyzed the performance of our design in terms of quantum cost, quantum delay and ancillary bits. Finally, the simulation verifies the correctness of our design.

Image Representation, Filtering, and Natural Computing in a Multivalued Quantum System

2016 ◽  
pp. 689-717 ◽  
Author(s):  
Sanjay Chakraborty ◽  
Lopamudra Dey
Keyword(s):  
Image Processing ◽  
Quantum System ◽  
Quantum Physics ◽  
Image Representation ◽  
Quantum Image Processing ◽  
Natural Computing ◽  
Quantum Image ◽  
Image Pixels ◽  
Classical Image ◽  
And Storage

Image processing on quantum platform is a hot topic for researchers now a day. Inspired from the idea of quantum physics, researchers are trying to shift their focus from classical image processing towards quantum image processing. Storing and representation of images in a binary and ternary quantum system is always one of the major issues in quantum image processing. This chapter mainly deals with several issues regarding various types of image representation and storage techniques in a binary as well as ternary quantum system. How image pixels can be organized and retrieved based on their positions and intensity values in 2-states and 3-states quantum systems is explained here in detail. Beside that it also deals with the topic that focuses on the clear filteration of images in quantum system to remove unwanted noises. This chapter also deals with those important applications (like Quantum image compression, Quantum edge detection, Quantum Histogram etc.) where quantum image processing associated with some of the natural computing techniques (like AI, ANN, ACO, etc.).

scholarly journals Evaluation of Several Feature Detectors/Extractors on Underwater Images towards vSLAM

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4343
Author(s):  
Franco Hidalgo ◽  
Thomas Bräunl
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Processing Time ◽  
Common Interest ◽  
Interest Point ◽  
Visual Slam ◽  
Interest Points ◽  
Interest Point Detectors ◽  
Feature Detectors ◽  
Video Frames

Modern visual SLAM (vSLAM) algorithms take advantage of computer vision developments in image processing and in interest point detectors to create maps and trajectories from camera images. Different feature detectors and extractors have been evaluated for this purpose in air and ground environments, but not extensively for underwater scenarios. In this paper (I) we characterize underwater images where light and suspended particles alter considerably the images captured, (II) evaluate the performance of common interest points detectors and descriptors in a variety of underwater scenes and conditions towards vSLAM in terms of the number of features matched in subsequent video frames, the precision of the descriptors and the processing time. This research justifies the usage of feature detectors in vSLAM for underwater scenarios and present its challenges and limitations.

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