Quantum Image Segmentation Algorithms

2021 ◽  
pp. 246-267
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Search Algorithm ◽  
Detection Algorithm ◽  
The Other ◽  
Quantum Image Processing ◽  
Quantum Image ◽  
Edge Detection Algorithm ◽  
Segmentation Algorithms ◽  
Grover Search

Quantum image segmentation has always been one of the difficult tasks in quantum image processing. This chapter introduce two quantum image segmentation algorithms. One is quantum edge detection algorithm; the other one is quantum image segmentation based on generalized Grover search algorithm.

Research and Simulation on the Image Segmentation Algorithm with Corn Image Based on HSI Color Model

2012 ◽  
Vol 459 ◽  
pp. 128-131
Author(s):  
Xue Feng Hou ◽  
Yuan Yuan Shang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Digital Image Processing ◽  
Detection Algorithm ◽  
Color Model ◽  
Edge Detection Algorithm ◽  
Segmentation Algorithms ◽  
Hsi Color Model ◽  
Classical Image ◽  
Image Segmentation Algorithm

Image segmentation is one focus of digital image processing. In this paper, fourteen different kinds of classical image segmentation algorithms are studied and compared using corn image and simulating in MATLAB based on HSI color model. The result reveals that the method that using H component based on HSI color model to deal with the histogram threshold algorithm and Laplace edge detection algorithm is effectively extract the plant from the corn image

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.).

scholarly journals Hardware Implementation of Sobel Edge Detection Algorithm

2020 ◽  
Vol 32 ◽  
pp. 03051
Author(s):  
Ankita Pujare ◽  
Priyanka Sawant ◽  
Hema Sharma ◽  
Khushboo Pichhode
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Feature Detection ◽  
Hardware Implementation ◽  
Software Tool ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Sobel Edge Detection ◽  
Image Edges

In the fields of image processing, feature detection, the edge detection is an important aspect. For detection of sharp changes in the properties of an image, edges are recognized as important factors which provides more information or data regarding the analysis of an image. In this work coding of various edge detection algorithms such as Sobel, Canny, etc. have been done on the MATLAB software, also this work is implemented on the FPGA Nexys 4 DDR board. The results are then displayed on a VGA screen. The implementation of this work using Verilog language of FPGA has been executed on Vivado 18.2 software tool.

Improved Algorithm for Sub-Pixel Edge Detection Based on Zernike Moments

2011 ◽  
Vol 341-342 ◽  
pp. 763-767
Author(s):  
Bao Yong Zhao ◽  
Ying Jian Qi
Keyword(s):  
Edge Detection ◽  
Detection Algorithm ◽  
Zernike Moments ◽  
The Other ◽  
Original Image ◽  
Edge Detection Algorithm ◽  
Series Of Experiments ◽  
The One ◽  
New Criterion ◽  
Improved Algorithm

The principle of Zernike moments and the method of sub-pixel edge detection based on Zernike moments were introduced in this paper. With the consideration of the limitation of the sub-pixel edge detection algorithm by Ghosal, such as the lower location precision of the edge and the extracted wider edge than that of the original image, an improved algorithm was proposed. On the one hand, a mask of size nine multiply nine was calculated and could be applied for the edge detection. On the other hand, a new criterion for edge detection was put forward. Additionally, a series of experiments were designed and implemented. The experiment results show that accuracy of the improved algorithm is higher than that obtained from using other size templates and Ghosal algorithm.

A Survey on Quantum Image Processing

2018 ◽  
Vol 27 (4) ◽  
pp. 718-727 ◽  
Author(s):  
Yongquan Cai ◽  
Xiaowei Lu ◽  
Nan Jiang
Keyword(s):  
Image Processing ◽  
Quantum Image Processing ◽  
Quantum Image
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