scholarly journals TXI: Texture and Color Enhancement Imaging for Endoscopic Image Enhancement

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tomoya Sato
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Experimental Results ◽  
Clinical Tool ◽  
Color Changes ◽  
Endoscopic Image ◽  
Mucosal Surfaces ◽  
Gastrointestinal Lesions ◽  
Tissue Differences ◽  
Color Enhancement

Recognition of lesions with subtle morphological and/or color changes during white light imaging (WLI) endoscopy remains a challenge. Often the endoscopic image suffers from nonuniform illumination across the image due to curvature in the lumen and the direction of the illumination light of the endoscope. We propose an image enhancement technology to resolve the drawbacks above called texture and color enhancement imaging (TXI). TXI is designed to enhance three image factors in WLI (texture, brightness, and color) in order to clearly define subtle tissue differences. In our proposed method, retinex-based enhancement is employed in the chain of endoscopic image processing. Retinex-based enhancement is combined with color enhancement to greatly accentuate color tone differences of mucosal surfaces. We apply TXI to animal endoscopic images and evaluate the performance of TXI compared with conventional endoscopic enhancement technologies, conventionally used techniques for real-world image processing, and newly proposed techniques for surgical endoscopic image augmentation. Our experimental results show that TXI can enhance brightness selectively in dark areas of an endoscopic image and can enhance subtle tissue differences such as slight morphological or color changes while simultaneously preventing over-enhancement. These experimental results demonstrate the potential of the proposed TXI algorithm as a future clinical tool for detecting gastrointestinal lesions having difficult-to-recognize tissue differences.

Probabilistic, Multi-Scale Fractional Tonal Correction Bilateral Filter-Based Hazy Image Enhancement

2020 ◽  
Vol 20 (02) ◽  
pp. 2050010
Author(s):  
U. A. Nnolim
Keyword(s):  
Image Enhancement ◽  
Fractional Order ◽  
Bilateral Filter ◽  
Experimental Results ◽  
Bilateral Filtering ◽  
Image Intensity ◽  
Multi Scale ◽  
Global Contrast ◽  
Color Enhancement ◽  
Color Cast

This paper describes an algorithm utilizing a modified multi-scale fractional order-based operator combined with a probabilistic tonal operator, adaptive color enhancement and bilateral filtering to process hazy and underwater images. The multi-scale algorithm complements the tonal operator by enhancing edges, preventing overexposure of bright image regions, while enhancing details in the dark areas. The addition of a previously developed global enhancement operator removes color cast and improves global contrast in underwater images. The color enhancement function augments the color results of the dehazing algorithm without distorting image intensity. Furthermore, the bilateral filter suppresses noise while preserving enhanced details/edges due to the multi-scale algorithm. Experimental results indicate that the proposed system yields comparable or better results than other algorithms from the literature.

Local Statistic Enhancement of Digital Image Based on Genetic Algorithm

2013 ◽  
Vol 427-429 ◽  
pp. 1836-1840 ◽  
Author(s):  
Yong Zhuo Wu ◽  
Zhen Tu ◽  
Lei Liu
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Image Enhancement ◽  
Digital Image Processing ◽  
Digital Image ◽  
Experimental Results ◽  
Computer Application ◽  
Novel Method ◽  
New Research

Iamge repair using the digital image processing technology has become a new research point in computer application. A novel method of local statistic enhancement based on genetic algorithm is proposed in this paper for the image enhancement. The modified amplified function are used as the jugement criterion, and the optimal paremeters are searched by the genetic algorithm. Experimental results show that the quality of images is improved dramatically by using this method.

A WEARABLE DOCUMENT READER FOR THE VISUALLY IMPAIRED: DEWARPING AND SEGMENTATION

2009 ◽  
Vol 18 (03) ◽  
pp. 467-486 ◽  
Author(s):  
ROBERT KEEFER ◽  
PRAVEEN KAKUMANU ◽  
NIKOLAOS BOURBAKIS
Keyword(s):  
Image Processing ◽  
Word Recognition ◽  
Visual Impairment ◽  
Image Enhancement ◽  
Visually Impaired ◽  
Experimental Results ◽  
Image Capture ◽  
Region Segmentation ◽  
Image Stabilization ◽  
Reading Task

While reading devices for the visually impaired have been available for many years, they are often expensive and difficult to use. The image processing required to enable the reading task is a composition of several important sub-tasks, such as image capture, image stabilization, image enhancement and page-curl dewarping region segmentation, regions grouping, and word recognition In this paper we deal with some of these sub-tasks in an effort to prototype a device (Tyflos-reader) that will read a document for a person with a visual impairment and respond to voice commands for control. Initial experimental results on a set of textbook and newspaper pages are also presented.

An Image Segmentation Method Using Image Enhancement and PCNN with Adaptive Parameters

2012 ◽  
Vol 490-495 ◽  
pp. 1251-1255 ◽  
Author(s):  
Hong Cai ◽  
Xue Yuan Zhang ◽  
Hai Tao Dai ◽  
Dong Ming Zhou
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Image Enhancement ◽  
Adaptive Algorithm ◽  
Experimental Results ◽  
Edge Extraction ◽  
Segmentation Method ◽  
Adaptive Parameters ◽  
Selection Of ◽  
Better Than

PCNN model is particularly suitable for image segmentation and edge extraction, but its effect depends on the selection of parameters in PCNN model and network iteration settings, which needs for a large number of artificial interaction and has limited PCNN image processing practicality. In this paper, through combining statistical properties of images and PCNN model, we present an adaptive algorithm based on the distribution of pixels to replace the artificial interaction. Experimental results show that image segmentation using image enhancement and PCNN with adaptive parameters is significantly better than the traditional PCNN image segmentation and verify the effectiveness of the method.

Developing an Inkjet Printer I: RGB Image to CMY Ink Amounts - Image Processing and Color Management

2020 ◽  
Vol 2020 (15) ◽  
pp. 350-1-350-10
Author(s):  
Yin Wang ◽  
Baekdu Choi ◽  
Davi He ◽  
Zillion Lin ◽  
George Chiu ◽  
...  
Keyword(s):  
Image Processing ◽  
Low Cost ◽  
Experimental Results ◽  
Color Management ◽  
Finger Nail ◽  
Inkjet Printer ◽  
Rgb Image ◽  
Novel Algorithm

In this paper, we will introduce a novel low-cost, small size, portable nail printer. The usage of this system is to print any desired pattern on a finger nail in just a few minutes. The detailed pre-processing procedures will be described in this paper. These include image processing to find the correct printing zone, and color management to match the patterns’ color. In each phase, a novel algorithm will be introduced to refine the result. The paper will state the mathematical principles behind each phase, and show the experimental results, which illustrate the algorithms’ capabilities to handle the task.

Implications of Digital Image Processing in the Paraclinical Assessment of the Partially Edentated Patient

2018 ◽  
Vol 69 (2) ◽  
pp. 521-524
Author(s):  
Magda Ecaterina Antohe ◽  
Doriana Agop Forna ◽  
Cristina Gena Dascalu ◽  
Norina Consuela Forna
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Treatment Plan ◽  
Digital Processing ◽  
Contour Detection ◽  
Anatomical Structures ◽  
Imaging Processing ◽  
Point To Point ◽  
Radiological Image ◽  
Processing Techniques

The application of certain digital processing techniques offers the possibility of extra accuracy in the interpretation of paraclinical examinations of this type, with profound implications in the diagnosis as well as in the hierarchy of the treatment plan. The purpose of this study is to identify the type of imaging processing for the identification of pathological elements from orthopantomographies and articular tomographies. A number of 20 orthopantomographies and 15 temporo-mandibular joint tomography have undergone through various image enhancement techniques. Various methods of image enhancement (enhancement) have been used for those procedures whereby it becomes more useful in the following aspects: specific details are highlighted; noise is eliminated; the image becomes more visually attractive. The workings were done in Corel PhotoPaint 7.0, using the automatic procedures available.The choice of a particular type of image enhancement technique has been selected for each type of pathology found in orthopantomographies or articular tomography, providing the best accuracy for an optimal imaging interpretation that underpins a precision diagnosis.Of the most useful imaging processing in the optimization of the orthopantomographic image accuracy the point-to-point transformations are to be noted. The image processing proposed in this article focused primarily on improving the radiological image attributes to highlight specific anatomical structures, and secondly, the contour detection, where it was necessary for the diagnostic purposes as well.

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

A μc-Si:H P-I-N Imager For 2-D Pattern Recognition

1999 ◽  
Vol 558 ◽  
Author(s):  
J. Martins ◽  
M. Fernandes ◽  
F. Sousa ◽  
P. Louro ◽  
A. MaçArico ◽  
...  
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Image Enhancement ◽  
Image Representation ◽  
Rectangular Grid ◽  
Sensing Element ◽  
Coplanar Electrodes ◽  
Basic Image ◽  
Image Processing Algorithms ◽  
Processing Algorithms

ABSTRACTA TCO/ μc-p-i-n Si:H/AI imager is presented and analyzed. The μc-p-i-n Si:H photodiode acts as a sensing element. Contacts are used as an electrical interface. The image is acquired by a scan-out process. Sampling is performed on a rectangular grid, and the read-out of the photogenerated charges is achieved by measuring simultaneously both transverse photovoltages at the coplanar electrodes. The image representation in gray-tones is obtained by using low level processing algorithms. Basic image processing algorithms are developed for image enhancement and restoration.

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