scholarly journals Image processing system using MATLAB-based analytics

2021 ◽  
Vol 10 (5) ◽  
pp. 2566-2577
Author(s):  
Gerald K. Ijemaru ◽  
Augustine O. Nwajana ◽  
Emmanuel U. Oleka ◽  
Richard I. Otuka ◽  
Isibor K. Ihianle ◽  
...  
Keyword(s):  
Image Processing ◽  
Processing System ◽  
Image Editing ◽  
Technological Advancement ◽  
Matlab Toolbox ◽  
Processing Application ◽  
Video Images ◽  
Image Processing Application ◽  
Processing Techniques ◽  
Blur Removal

Owing to recent technological advancement, computers and other devices running several image editing applications can be further exploited for digital image processing operations. This paper evaluates various image processing techniques using matrix laboratory (MATLAB-based analytics). Compared to the conventional techniques, MATLAB gives several advantages for image processing. MATLAB-based technique provides easy debugging with extensive data analysis and visualization, easy implementation and algorithmic-testing without recompilation. Besides, MATLAB's computational codes can be enhanced and exploited to process and create simulations of both still and video images. Moreover, MATLAB codes are much concise compared to C++, thus making it easier for perusing and troubleshooting. MATLAB can handle errors prior to execution by proposing various ways to make the codes faster. The proposed technique enables advanced image processing operations such as image cropping/resizing, image denoising, blur removal, and image sharpening. The study aims at providing readers with the most recent MATLAB-based image processing application-tools. We also provide an empirical-based method using two-dimensional discrete cosine transform (2D-DCT) derived from its coefficients. Using the most recent algorithms running on MATLAB toolbox, we performed simulations to evaluate the performance of our proposed technique. The results largely present MATLAB as a veritable approach for image processing operations.

Bubble Image Processing Based on ARM in Gas-Liquid Two-Phase Flow

2013 ◽  
Vol 694-697 ◽  
pp. 2630-2633
Author(s):  
Ting Xue ◽  
Qing Fang Wang ◽  
Xiao Wen Liu
Keyword(s):  
Image Processing ◽  
Two Phase Flow ◽  
Processing System ◽  
Phase Flow ◽  
Two Phase ◽  
Processing Application ◽  
Embedded Platform ◽  
Time Performance ◽  
Image Processing Application ◽  
Bubble Characteristics

Morphology and characteristics of bubbles have an important effect on engineering applications of gas-liquid two-phase flow. To accurately describe the bubble characteristics, the bubble image processing system based on ARM9 is designed. System hardware is constructed with S3C2440 as the core. QT/Embedded and Qtopia are transplanted to the embedded platform, and the image processing application is developed using QT/Embedded, realizing a good human-computer interaction. According to the characteristics of bubble image, image processing algorithms are presented. The System has low power consumption, small size, and a good real-time performance and scalability.

DFOG-Image Processing Application for Real-Time Defogging

2020 ◽  
Author(s):  
Indiketiya I.H.O.H ◽  
Kulasekara K.M.R.A ◽  
J.M. Thomas ◽  
Ishara Gamage ◽  
Thusithanjana Thilakarathna
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Processing Application ◽  
Image Processing Application

Formulation of Fractional Derivative-Based De-Hazing Algorithm and Implementation on Mobile-Embedded Devices

2019 ◽  
Vol 19 (01) ◽  
pp. 1950003
Author(s):  
Uche A. Nnolim
Keyword(s):  
Image Processing ◽  
Fractional Derivative ◽  
Mobile Devices ◽  
Fractional Order ◽  
Spatial Filter ◽  
Embedded Devices ◽  
Processing Application ◽  
Fractional Order Calculus ◽  
Image Processing Application ◽  
Logarithmic Image Processing

This paper presents the modification of a previously developed algorithm using fractional order calculus and its implementation on mobile-embedded devices such as smartphones. The system performs enhancement on three categories of images such as those exhibiting uneven illumination, faded features/colors and hazy appearance. The key contributions include the simplified scheme for illumination correction, contrast enhancement and de-hazing using fractional derivative-based spatial filter kernels. These are achieved without resorting to logarithmic image processing, histogram-based statistics and complex de-hazing techniques employed by conventional algorithms. The simplified structure enables ease of implementation of the algorithm on mobile devices as an image processing application. Results indicate that the fractional order version of the algorithm yields good results relative to the integer order version and other algorithms from the literature.

Sign in / Sign up

Export Citation Format

Share Document