image process
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Author(s):  
Waheed Muhammad SANYA ◽  
Gaurav BAJPAI ◽  
Haji Ali HAJI

Vision relieves humans to understand the environmental deviations over a period. These deviations are seen by capturing the images. The digital image plays a dynamic role in everyday life. One of the processes of optimizing the details of an image whilst removing the random noise is image denoising. It is a well-explored research topic in the field of image processing. In the past, the progress made in image denoising has advanced from the improved modeling of digital images. Hence, the major challenges of the image process denoising algorithm is to advance the visual appearance whilst preserving the other details of the real image. Significant research today focuses on wavelet-based denoising methods. This research paper presents a new approach to understand the Sobel imaging process algorithm on the Linux platform and develop an effective algorithm by using different optimization techniques on SABRE i.MX_6. Our work concentrated more on the image process algorithm optimization. By using the OpenCV environment, this paper is intended to simulate a Salt and Pepper noisy phenomenon and remove the noisy pixels by using Median Filter Algorithm. The Sobel convolution method included and used in the design of a Sobel Filter and then process the image following the median filter, to achieve an effective edge detection result. Finally, this paper optimizes the algorithm on SABRE i.MX_6 Linux environment. By using algorithmic optimization (lower complexity algorithm in the mathematical sense, using appropriate data structures), optimization for RISC (loop unrolling) processors, including optimization for efficient use of hardware resources (access to data, cache management and multi-thread), this paper analyzed the different response parameters of the system with varied inputs, different compiler options (O1, O2, or O3), and different doping degrees. The proposed denoising algorithm shows the meaningful addition of the visual quality of the images and the algorithmic optimization assessment.


2021 ◽  
Vol 38 (1) ◽  
pp. 015001
Author(s):  
Yanan Zhao ◽  
Chunlin Wu ◽  
Qiaoli Dong ◽  
Yufei Zhao

Abstract We consider a wavelet based image reconstruction model with the ℓ p (0 < p < 1) quasi-norm regularization, which is a non-convex and non-Lipschitz minimization problem. For solving this model, Figueiredo et al (2007 IEEE Trans. Image Process. 16 2980–2991) utilized the classical majorization-minimization framework and proposed the so-called Isoft algorithm. This algorithm is computationally efficient, but whether it converges or not has not been concluded yet. In this paper, we propose a new algorithm to accelerate the Isoft algorithm, which is based on Nesterov’s extrapolation technique. Furthermore, a complete convergence analysis for the new algorithm is established. We prove that the whole sequence generated by this algorithm converges to a stationary point of the objective function. This convergence result contains the convergence of Isoft algorithm as a special case. Numerical experiments demonstrate good performance of our new algorithm.


2021 ◽  
Vol 9 (11) ◽  
pp. 635-638
Author(s):  
Mrs. Asha K H ◽  
Manjunathswamy B E ◽  
Mrs. Chaithra A S

The main goal of the Image Process project is to extract important information from photographs. The machine may produce a description, interpretation, and comprehension of the scene based on this extracted data. The main goal of image processing is to transform photos in the desired way. This technique allows users to obtain the text of picture processing printing processes and to save the data to disc in a variety of formats. In other terms, image processing is the process of neutering and analysing graphical information in photographs. In our lives, we frequently come across many types of image processing. The clearest example of image processing in our lives is our brain's perceiving of visuals. Once we perceive pictures with our eyes, the process takes relatively little time


2021 ◽  
Vol 30 (7) ◽  
pp. 91-104
Author(s):  
M. A. Lukashenko ◽  
N. V. Gromova ◽  
A. A. Ozhgikhina

Digitalization of business and society inevitably affects almost all spheres, and education is not an exception. New high-tech tools and solutions are rapidly coming into this industry, without which further development and implementation of the educational process is no longer possible. Today target audience of any educational organizations is informationally advanced and prefers to source useful information from social networks, often making business decisions based on it. Such conditions put forward new requirements for educational organizations to increase their activity in social networks and, above all, to form a digital image. Professors are the face of any educational organization. Inasmuch as they are the subjects who directly interact with students, their personal digital image plays an important role in shaping corporate image of the university. Relevance of this article is in the research of the digital image process formation by professors of business universities, which are the flagships and market-oriented subjects of local higher education.The article aims to identify the current state of forming the digital image of teachers of entrepreneurial universities in social networks. To achieve the goal, the article discusses the features of an entrepreneurial university and the characteristics of its corporate image, shows the need to form a teacher’s digital image and identifies strategies for such formation in social networks. The study of the activity of Russian entrepreneurial universities’ teachers in social networks was carried out and the comparison of the results with the similar activity of foreign universities’ teachers was made.


Author(s):  
Arjun Dileep

Abstract: In today's world, nearly everything we have a tendency to do has been simplified by machine-driven tasks. In a trial to specialize in the road whereas driving, drivers usually miss out on signs on the facet of the road, that can be dangerous for them and for the folks around them. This drawback may be avoided if there was AN economical thanks to inform the motive force while not having them to shift their focus. Traffic Sign Detection and Recognition (TSDR) plays a vital role here by detection and recognizing a symptom, therefore notifying the motive force of any coming signs. This not solely ensures road safety, however additionally permits the motive force to be at very little a lot of ease whereas driving on tough or new roads. Another normally long-faced drawback isn't having the ability to know the which means of the sign. With the assistance of this Advanced Driver help Systems (ADAS) application, drivers can not face the matter of understanding what the sign says. during this paper, we have a tendency to propose a way for Traffic Sign Detection and Recognition exploitation image process for the detection of a symptom and a Convolutional Neural Networks (CNN) for the popularity of the sign. CNNs have a high recognition rate, therefore creating it fascinating to use for implementing varied laptop vision tasks. TensorFlow is employed for the implementation of the CNN. Keywords: actitvity recognition; knowledge collection; knowledge preprocessing; coaching CNN model ;evaluating model; predicting the result.


Author(s):  
Faisal Rehman ◽  
◽  
Syed Sheeraz Ali ◽  
Hamadullah Panhwar ◽  
Dr. Akhtar Hussain Phul ◽  
...  

In the medical era the Brain tumor is one of the most important research areas in the field of medical sciences. Researcher are trying to find the reliable and cost effective medical equipment’s for the cancer and its type for the diagnosed, especially tumor has deferent kinds but the major two type are discussed in this research paper. Which are the benign and Pre-Malignant, this research work is proposed for these factors such as the accuracy of the MRI image for the tumor identification and actual placing were taken into consideration. In this study, an algorithm is proposed to detect the brain tumor from magnetic resonance image (MRI) data simple. As enhance the image quality for the easiness the tumor treatments and diagnosed for the patients. The proposed algorithm enhances the MR image quality and detects the Brain tumor which helps the Physician to diagnose the tumor easily. As well this algorithm automatically calculates the area of tumor, size and location of the tumor where it is present for diagnostic the Patient.


Author(s):  
Faisal Rehman ◽  
◽  
Syed Sheeraz Ali ◽  
Hamadullah Panhwar ◽  
Dr. Akhtar Hussain Phul ◽  
...  

In the medical era the Brain tumor is one of the most important research areas in the field of medical sciences. Researcher are trying to find the reliable and cost effective medical equipment’s for the cancer and its type for the diagnosed, especially tumor has deferent kinds but the major two type are discussed in this research paper. Which are the benign and Pre-Malignant, this research work is proposed for these factors such as the accuracy of the MRI image for the tumor identification and actual placing were taken into consideration. In this study, an algorithm is proposed to detect the brain tumor from magnetic resonance image (MRI) data simple. As enhance the image quality for the easiness the tumor treatments and diagnosed for the patients. The proposed algorithm enhances the MR image quality and detects the Brain tumor which helps the Physician to diagnose the tumor easily. As well this algorithm automatically calculates the area of tumor, size and location of the tumor where it is present for diagnostic the Patient.


2021 ◽  
Vol 13 (16) ◽  
pp. 3062
Author(s):  
Guo Zhang ◽  
Boyang Jiang ◽  
Taoyang Wang ◽  
Yuanxin Ye ◽  
Xin Li

To ensure the accuracy of large-scale optical stereo image bundle block adjustment, it is necessary to provide well-distributed ground control points (GCPs) with high accuracy. However, it is difficult to acquire control points through field measurements outside the country. Considering the high planimetric accuracy of spaceborne synthetic aperture radar (SAR) images and the high elevation accuracy of satellite-based laser altimetry data, this paper proposes an adjustment method that combines both as control sources, which can be independent from GCPs. Firstly, the SAR digital orthophoto map (DOM)-based planar control points (PCPs) acquisition is realized by multimodal matching, then the laser altimetry data are filtered to obtain laser altimetry points (LAPs), and finally the optical stereo images’ combined adjustment is conducted. The experimental results of Ziyuan-3 (ZY-3) images prove that this method can achieve an accuracy of 7 m in plane and 3 m in elevation after adjustment without relying on GCPs, which lays the technical foundation for a global-scale satellite image process.


Author(s):  
Kalyani A. Sonwane

Self-driving cars became a trending subject with a big improvement in technologies within the last decade. The project aims to coach a neural network to drive associate degree autonomous automobile agent on Udacity’s automobile Simulator's tracks. Udacity has discharged the machine as ASCII text file computer code and enthusiasts have hosted a contest (challenge) to show an automobile the way to drive victimisation solely camera pictures and deep learning. Autonomously driving an automobile needs learning to regulate steering angle, throttle and brakes. The activity biological research technique is employed to mimic human driving behaviour within the coaching model on the track. which means a dataset is generated within the machine by a user-driven automobile in coaching mode, and therefore the deep neural network model then drives the automobile in autonomous mode. 3 architectures area unit compared regarding their performance. Though the models performed well for the track it had been trained with, the important challenge was to generalize this behaviour on a second track out there on the machine. The dataset for Track_1, that was straightforward with favourable road conditions to drive, was used because the coaching set to drive the automobile autonomously on Track_2, consisting of sharp turns, barriers, elevations, and shadows. Image process and completely different augmentation techniques were accustomed tackle this downside, that allowed extracting the maximum amount data and options within the knowledge as doable. Ultimately, the automobile was ready to run on Track_2 generalizing well. The project aims at reaching an equivalent accuracy on period of time knowledge within the future.


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