scholarly journals Image Enhancement by Histogram Specification Using Multiple Target Images

2011 ◽  
pp. 29-37
Author(s):  
Pavithra P ◽  
Ramyashree N ◽  
Shruthi T.V ◽  
Dr. Jharna Majumdar
Keyword(s):  
High Resolution ◽  
Image Enhancement ◽  
Input Image ◽  
Multiple Target ◽  
Target Image ◽  
Image Histogram ◽  
Histogram Specification ◽  
High Resolution Images

Shape and characteristics of the histogram plays a major role in finding the quality of an image. Histogram Specification is an image enhancement technique, where the histogram of the input image is transformed to a pre-specified histogram derived from a high resolution image, called target image. In this paper, the classical histogram specification technique is extended by using a target image which is obtained by fusing multiple high resolution images. A set of Quality Metrics were identified to assess the quality of the output enhanced image. The paper addresses the following issues: a) Effect of varying the number of target images on the quality of the output enhanced image b) Role of using different methods of fusion on the quality of the output enhanced image c) Category of the target image on the quality of the output enhanced image. If the input image is from a forest, whether in order to obtain an enhanced image, all target images has to be selected from the forest category d) Effect of preprocessing of target image on the quality of the output enhanced image.

scholarly journals Cloud Computation Using High-Resolution Images for Improving the SDG Indicator on Open Spaces

2020 ◽  
Vol 12 (7) ◽  
pp. 1144
Author(s):  
Rosa Aguilar ◽  
Monika Kuffer
Keyword(s):  
High Resolution ◽  
Open Space ◽  
Local Planning ◽  
Open Spaces ◽  
High Resolution Imagery ◽  
Coarse Spatial Resolution ◽  
Multi Temporal ◽  
High Resolution Images ◽  
Cloud Computation

Open spaces are essential for promoting quality of life in cities. However, accelerated urban growth, in particular in cities of the global South, is reducing the often already limited amount of open spaces with access to citizens. The importance of open spaces is promoted by SDG indicator 11.7.1; however, data on this indicator are not readily available, neither globally nor at the metropolitan scale in support of local planning, health and environmental policies. Existing global datasets on built-up areas omit many open spaces due to the coarse spatial resolution of input imagery. Our study presents a novel cloud computation-based method to map open spaces by accessing the multi-temporal high-resolution imagery repository of Planet. We illustrate the benefits of our proposed method for mapping the dynamics and spatial patterns of open spaces for the city of Kampala, Uganda, achieving a classification accuracy of up to 88% for classes used by the Global Human Settlement Layer (GHSL). Results show that open spaces in the Kampala metropolitan area are continuously decreasing, resulting in a loss of open space per capita of approximately 125 m2 within eight years.

scholarly journals The Role of Endoscopic Ultrasound in the Diagnosis of Gallbladder Lesions

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1789
Author(s):  
Senju Hashimoto ◽  
Kazunori Nakaoka ◽  
Naoto Kawabe ◽  
Teiji Kuzuya ◽  
Kohei Funasaka ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
High Resolution ◽  
Endoscopic Ultrasound ◽  
Wall Thickness ◽  
Fine Needle Aspiration ◽  
Needle Aspiration ◽  
Histological Diagnosis ◽  
Fine Needle ◽  
High Resolution Images

Gallbladder (GB) diseases represent various lesions including gallstones, cholesterol polyps, adenomyomatosis, and GB carcinoma. This review aims to summarize the role of endoscopic ultrasound (EUS) in the diagnosis of GB lesions. EUS provides high-resolution images that can improve the diagnosis of GB polypoid lesions, GB wall thickness, and GB carcinoma staging. Contrast-enhancing agents may be useful for the differential diagnosis of GB lesions, but the evidence of their effectiveness is still limited. Thus, further studies are required in this area to establish its usefulness. EUS combined with fine-needle aspiration has played an increasing role in providing a histological diagnosis of GB tumors in addition to GB wall thickness.

scholarly journals A Review on Deep Image Contrast Enhancement

2020 ◽  
Vol 6 (1) ◽  
pp. 4
Author(s):  
Puspad Kumar Sharma ◽  
Nitesh Gupta ◽  
Anurag Shrivastava
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Image Enhancement ◽  
Research Work ◽  
Atmospheric Condition ◽  
Input Image ◽  
Learning Approaches ◽  
Sensing Applications ◽  
Image Contrast Enhancement

In image processing applications, one of the main preprocessing phases is image enhancement that is used to produce high quality image or enhanced image than the original input image. These enhanced images can be used in many applications such as remote sensing applications, geo-satellite images, etc. The quality of an image is affected due to several conditions such as by poor illumination, atmospheric condition, wrong lens aperture setting of the camera, noise, etc [2]. So, such degraded/low exposure images are needed to be enhanced by increasing the brightness as well as its contrast and this can be possible by the method of image enhancement. In this research work different image enhancement techniques are discussed and reviewed with their results. The aim of this study is to determine the application of deep learning approaches that have been used for image enhancement. Deep learning is a machine learning approach which is currently revolutionizing a number of disciplines including image processing and computer vision. This paper will attempt to apply deep learning to image filtering, specifically low-light image enhancement. The review given in this paper is quite efficient for future researchers to overcome problems that helps in designing efficient algorithm which enhances quality of the image.

scholarly journals Image Enhancement Methods for Remote Sensing: A Survey

2021 ◽  
Author(s):  
Nur Huseyin Kaplan ◽  
Isin Erer ◽  
Deniz Kumlu
Keyword(s):  
Remote Sensing ◽  
Image Enhancement ◽  
Hybrid Methods ◽  
Input Image ◽  
Transform Domain ◽  
Histogram Modification ◽  
Contrast Gain ◽  
Gain Enhancement ◽  
Color Preservation

The quality of the images obtained from remote sensing devices is very important for many image processing applications. Most of the enhancement methods are based on histogram modification and transform based methods. Histogram modification based methods aim to modify the histogram of the input image to obtain a more uniform distribution. Transform based methods apply a certain transform to the input image and enhance the image in transform domain followed by the inverse transform. In this work, both histogram modification and transform domain methods have been considered, as well as hybrid methods. Moreover, a new hybrid algorithm is proposed for remote sensing image enhancement. Visual comparisons as well as quantitative comparisons have been carried out for different enhancement methods. For objective comparison quality metrics, namely Contrast Gain, Enhancement Measurement, Discrete Entropy and Average Mean Brightness Error have been used. The comparisons show that, the histogram modification methods have a better contrast improvement, while transform domain methods have a better performance in edge enhancement and color preservation. Moreover, hybrid methods which combine the two former approaches have higher potential.

scholarly journals Thick Cloud Removal in High-Resolution Satellite Images Using Stepwise Radiometric Adjustment and Residual Correction

2019 ◽  
Vol 11 (16) ◽  
pp. 1925 ◽  
Author(s):  
Zhiwei Li ◽  
Huanfeng Shen ◽  
Qing Cheng ◽  
Wei Li ◽  
Liangpei Zhang
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Real Data ◽  
Target Image ◽  
High Resolution Satellite Images ◽  
High Resolution Images ◽  
Residual Correction ◽  
Geographical Maps ◽  
Cloud Removal ◽  
Cloud Mask

Cloud cover is a common problem in optical satellite imagery, which leads to missing information in images as well as a reduction in the data usability. In this paper, a thick cloud removal method based on stepwise radiometric adjustment and residual correction (SRARC) is proposed, which is aimed at effectively removing the clouds in high-resolution images for the generation of high-quality and spatially contiguous urban geographical maps. The basic idea of SRARC is that the complementary information in adjacent temporal satellite images can be utilized for the seamless recovery of cloud-contaminated areas in the target image after precise radiometric adjustment. To this end, the SRARC method first optimizes the given cloud mask of the target image based on superpixel segmentation, which is conducted to ensure that the labeled cloud boundaries go through homogeneous areas of the target image, to ensure a seamless reconstruction. Stepwise radiometric adjustment is then used to adjust the radiometric information of the complementary areas in the auxiliary image, step by step, and clouds in the target image can be removed by the replacement with the adjusted complementary areas. Finally, residual correction based on global optimization is used to further reduce the radiometric differences between the recovered areas and the cloud-free areas. The final cloud removal results are then generated. High-resolution images with different spatial resolutions and land-cover change patterns were used in both simulated and real-data cloud removal experiments. The results suggest that SRARC can achieve a better performance than the other compared methods, due to the superiority of the radiometric adjustment and spatial detail preservation. SRARC is thus a promising approach that has the potential for routine use, to support applications based on high-resolution satellite images.

scholarly journals IMAGE RECONSTRUCTION WITH IMPROVED SUPER-RESOLUTION ALGORITHM

2004 ◽  
Vol 18 (08) ◽  
pp. 1513-1527
Author(s):  
CHIEN-YU CHEN ◽  
YU-CHUAN KUO ◽  
CHIOU-SHANN FUH
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Image Registration ◽  
Iterative Method ◽  
Image Enhancement ◽  
Super Resolution ◽  
Post Processing ◽  
High Resolution Images ◽  
Robust Image ◽  
Resolution Algorithm

In this paper we propose a technique that reconstructs high-resolution images with improved super-resolution algorithms, based on Irani and Peleg iterative method, and employs our suggested initial interpolation, robust image registration, automatic image selection and image enhancement post-processing. When the target of reconstruction is a moving object with respect to a stationary camera, high-resolution images can still be reconstructed, whereas previous systems only work well when we move the camera and the displacement of the whole scene is the same.

scholarly journals INTEGRATION OF POINT CLOUDS FROM TERRESTRIAL LASER SCANNING AND IMAGE-BASED MATCHING FOR GENERATING HIGH-RESOLUTION ORTHOIMAGES

2016 ◽  
Vol XLI-B5 ◽  
pp. 399-404 ◽  
Author(s):  
A. Salach ◽  
J.S. Markiewicza ◽  
D. Zawieska
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Digital Camera ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Historical Objects ◽  
Basic Objective ◽  
High Resolution Images

An orthoimage is one of the basic photogrammetric products used for architectural documentation of historical objects; recently, it has become a standard in such work. Considering the increasing popularity of photogrammetric techniques applied in the cultural heritage domain, this research examines the two most popular measuring technologies: terrestrial laser scanning, and automatic processing of digital photographs. The basic objective of the performed works presented in this paper was to optimize the quality of generated high-resolution orthoimages using integration of data acquired by a Z+F 5006 terrestrial laser scanner and a Canon EOS 5D Mark II digital camera. The subject was one of the walls of the “Blue Chamber” of the Museum of King Jan III’s Palace at Wilanów (Warsaw, Poland). The high-resolution images resulting from integration of the point clouds acquired by the different methods were analysed in detail with respect to geometric and radiometric correctness.

scholarly journals Pemetaan kualitas permukiman dengan menggunakan penginderaan jauh dan SIG di kecamatan Batam kota, Batam

2017 ◽  
Vol 31 (1) ◽  
pp. 39 ◽  
Author(s):  
M. Farizki ◽  
Wenang Anurogo
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Well Being ◽  
Google Earth ◽  
Remote Sensing Technology ◽  
Analysis Process ◽  
Sensing Technology ◽  
High Resolution Images

Permukiman adalah bagian dari lingkungan hidup yang berfungsi sebagai lingkungan tempat tinggal. Kondisi dari suatu permukiman sangat mempengaruhi kelangsungan kehidupan dan kesejahteraan makhluk hidup di permukiman tersebut. Untuk mengetahui kualitas permukiman di Kecamatan Batam Kota dibutuhkan parameter-parameter penentu yang di interpretasi dari citra resolusi tinggi (Google Earth). Untuk membantu proses analisis dengan memanfaatkan teknologi penginderaan jauh dan untuk pemetaan menggunakan software SIG. Metode yang digunakan adalah metode pengharkatan (scoring), tumpang susun (overlay). Hasil dari overlay tersebut adalah peta kualitas permukiman di kecamatan Batam Kota, Kota Batam. Dari penelitian ini dapat diketahui bahwa permukiman di Kecamatan Batam Kota dengan kualitas baik dengan luas 476.88 Ha, kualitas sedang dengan luas 650 Ha, dan kualitas buruk dengan luas 48.89 Ha. Dari hasil tersebut permukiman di Kecamatan Batam Kota didominasi oleh permukiman dengan kualitas sedang. The neighborhood is part of the environment that serves as a neighborhood residence. The condition of a settlement extremely affects to the continuity of life and the well-being of living creatures in the neighborhood. To find out the quality of the neighborhoods in districts of Batam City required parameters in determining the interpretation of high-resolution images (Google Earth). To help the analysis process by making use of remote sensing technology for the mapping, and using software SIG. The Method is using score (scoring), and stack bundles (overlay). The result of the overlay mapped quality neighborhoods in districts of Batam city, Batam city. From this research it can be known that settlements in Batam City with good quality with extensive 476.88 Ha, better quality with an area of 650 Hectares, and bad quality with extensive 48.89 Ha. The results of the neighborhoods in districts of Batam City are dominated by the neighborhoods with better quality.

scholarly journals Study of Feature Extraction of Retinal Scans

2019 ◽  
Vol 24 (1) ◽  
pp. 5-13
Author(s):  
Mohanad Abdulhamid ◽  
Gitonga Muthomi
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Image Enhancement ◽  
Computer System ◽  
Image Acquisition ◽  
Poor Quality ◽  
Design System ◽  
Security Systems ◽  
Matching Process ◽  
High Resolution Images

Abstract In this paper, the retina is discussed as part of the feature of extraction of retinal scans for use in security systems as a means of identification. The design system contains a method of image acquisition and processing of the image. A computer system is also incorporated for matching and verifying the image captured to an already present representation of unique features of the retina that are stored as templates for matching and identification. It should then either allow or deny the user depending on the results of the matching process. This paper shows the development of the step undertaken to process the image to the extraction of the features. The high resolution images are taken through a series of image enhancement process before feature extraction technics are applied and before templates are created for future referencing. The main limitation of this process is attributed to capturing the image from the retina. The image obtained may be of poor quality thus making the unique features of the retina unclear.

scholarly journals Retinal Image Enhancement using Ordering Gap Adjustment and Brightness Specification

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Image Enhancement ◽  
Retinal Image ◽  
Dynamic Range ◽  
Input Image ◽  
Image Contrast ◽  
Effective Algorithm ◽  
Problem Domain ◽  
Image Brightness ◽  
Image Histogram ◽  
Matlab Program

Color retinal image enhancement plays an important role in improving an image quality suited for reliable diagnosis. For this problem domain, a simple and effective algorithm for image contrast and color balance enhancement namely Ordering Gap Adjustment and Brightness Specification (OGABS) was proposed. The OGABS algorithm first constructs a specified histogram by adjusting the gap of the input image histogram ordering by its probability density function under gap limiter and Hubbard’s dynamic range specifications. Then, the specified histograms are targets to redistribute the intensity values of the input image based on histogram matching. Finally, color balance is improved by specifying the image brightness based on Hubbard’s brightness specification. The OGABS algorithm is implemented by the MATLAB program and the performance of our algorithm has been evaluated against data from STARE and DiaretDB0 datasets. The results obtained show that our algorithm enhances the image contrast and creates a good color balance in a pleasing natural appearance with a standard color of lesions.

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