An Image Enhancement Method Based on the Coal Mine Monitoring System

2012 ◽  
Vol 468-471 ◽  
pp. 204-207
Author(s):  
Zhen Chong Wang ◽  
Yan Qin Zhao
Keyword(s):  
Image Enhancement ◽  
Coal Mine ◽  
Impulse Noise ◽  
Histogram Equalization ◽  
Video Monitor ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Special Environment

For the low illumination and low contrast in the coal mine, images captured from the video monitor system sometimes are not so clear to help the related personal monitoring the production and safety of the mine. According to the special environment of coal mine, an image enhancement method was presented. In this method the impulse noise which is the mainly noise in the coal mine was first reduced with median filtering, then the low contrast and illumination was greatly improved with the improved adaptive histogram equalization. Experiments show that this method can improve the quality of images underground effectively.

scholarly journals An image enhancement method based on gabor filtering in wavelet domain and adaptive histogram equalization

2021 ◽  
Vol 21 (1) ◽  
pp. 146
Author(s):  
Jeevan K M ◽  
Anne Gowda A B ◽  
Padmaja Vijay Kumar
Keyword(s):  
Image Enhancement ◽  
Histogram Equalization ◽  
Median Filtering ◽  
Wavelet Domain ◽  
Gabor Filtering ◽  
Low Contrast ◽  
Enhancement Technique ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Two Parameters

<p><span>The images are not always good enough to convey the proper information. The image may be very bright or very dark sometime or it may be low contrast or high contrast. Because of these reasons image enhancement plays important role in digital image processing. In this paper we proposed an image enhancement technique in which Gabor and median filtering is performed in wavelet domain and Adaptive Histogram Equalization is performed in spatial domain. Brightness and contrast are the two parameters used for analyzing the performance of the proposed method</span></p>

Perbandingan Performa Histogram Equalization untuk Peningkatan Kualitas Gambar Minim Cahaya pada Android

2020 ◽  
Vol 12 (2) ◽  
pp. 80-88
Author(s):  
Claudia Kenyta ◽  
Daniel Martomanggolo Wonohadidjojo
Keyword(s):  
Image Enhancement ◽  
Light Condition ◽  
Histogram Equalization ◽  
Low Light ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Better Than

When the photos are taken in low light condition, the quality of the results will not meet their expectation. Image Enhancement method can be used to enhance the quality of the photos taken in low light condition. One of the algorithms used is called Histogram Equalization (HE), that works using Histogram basis. The superiority of HE algorithm in enhancing the quality of the photos taken in low light condition is the simplicity of the algorithm itself and it does not need a high specification device for the algorithm to run. One variant of HE algorithm is Contrast Limited Adaptive Histogram Equalization (CLAHE). This paper shows the implementation of HE algorithm and its performance in enhancing the quality of photos taken in low light condition on Android based application and the comparison with CLAHE algorithm. The results show that, HE algorithm is better than CLAHE algorithm.

Analysis of color Image Enhancement Using DWT, Wavelet Shrinkageand FHE Methods

2017 ◽  
Vol 7 (8) ◽  
pp. 56
Author(s):  
Ashish Dwivedi ◽  
Nirupma Tiwari
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Human Perception ◽  
Histogram Equalization ◽  
Wavelet Shrinkage ◽  
Visual Appearance ◽  
Enhancement Method ◽  
Fuzzy Histogram ◽  
Rgb Image

Image enhancement (IE) is very important in the field where visual appearance of an image is the main. Image enhancement is the process of improving the image in such a way that the resulting or output image is more suitable than the original image for specific task. With the help of image enhancement process the quality of image can be improved to get good quality images so that they can be clear for human perception or for the further analysis done by machines.Image enhancement method enhances the quality, visual appearance, improves clarity of images, removes blurring and noise, increases contrast and reveals details. The aim of this paper is to study and determine limitations of the existing IE techniques. This paper will provide an overview of different IE techniques commonly used. We Applied DWT on original RGB image then we applied FHE (Fuzzy Histogram Equalization) after DWT we have done the wavelet shrinkage on Three bands (LH, HL, HH). After that we fuse the shrinkage image and FHE image together and we get the enhance image.

Assessment of Some Enhancement Methods of Renal X-ray Image

2020 ◽  
Vol 18 (12) ◽  
pp. 01-05
Author(s):  
Salim J. Attia
Keyword(s):  
Image Quality ◽  
Radiation Dosage ◽  
Histogram Equalization ◽  
Quality Factors ◽  
X Ray ◽  
Adaptive Histogram Equalization ◽  
Quality Image ◽  
Enhancement Method ◽  
Spatial Quality

The study focuses on assessment of the quality of some image enhancement methods which were implemented on renal X-ray images. The enhancement methods included Imadjust, Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The images qualities were calculated to compare input images with output images from these three enhancement techniques. An eight renal x-ray images are collected to perform these methods. Generally, the x-ray images are lack of contrast and low in radiation dosage. This lack of image quality can be amended by enhancement process. Three quality image factors were done to assess the resulted images involved (Naturalness Image Quality Evaluator (NIQE), Perception based Image Quality Evaluator (PIQE) and Blind References Image Spatial Quality Evaluator (BRISQE)). The quality of images had been heightened by these methods to support the goals of diagnosis. The results of the chosen enhancement methods of collecting images reflected more qualified images than the original images. According to the results of the quality factors and the assessment of radiology experts, the CLAHE method was the best enhancement method.

scholarly journals Deep Learning Based Filtering Algorithm for Noise Removal in Underwater Images

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2742
Author(s):  
Aswathy K. Cherian ◽  
Eswaran Poovammal ◽  
Ninan Sajeeth Philip ◽  
Kadiyala Ramana ◽  
Saurabh Singh ◽  
...  
Keyword(s):  
Histogram Equalization ◽  
Noise Removal ◽  
Image Resolution ◽  
Similar Data ◽  
Underwater Imaging ◽  
Edge Information ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Water Sensing

Under-water sensing and image processing play major roles in oceanic scientific studies. One of the related challenges is that the absorption and scattering of light in underwater settings degrades the quality of the imaging. The major drawbacks of underwater imaging are color distortion, low contrast, and loss of detail (especially edge information). The paper proposes a method to address these issues by de-noising and increasing the resolution of the image using a model network trained on similar data. The network extracts frames from a video and filters them with a trigonometric–Gaussian filter to eliminate the noise in the image. It then applies contrast limited adaptive histogram equalization (CLAHE) to improvise the image contrast, and finally enhances the image resolution. Experimental results show that the proposed method could effectively produce enhanced images from degraded underwater images.

scholarly journals Cotton-wool spots, red-lesions and hard-exudates distinction using CNN enhancement and transfer learning

2021 ◽  
Vol 23 (2) ◽  
pp. 1170
Author(s):  
Tian-Swee Tan ◽  
M. A. As'ari ◽  
Wan Hazabbah Wan Hitam ◽  
Qi Zhe Ngoo ◽  
Matthias Tiong Foh thye ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Retinal Disease ◽  
Histogram Equalization ◽  
Disease Diagnosis ◽  
Training Dataset ◽  
Cotton Wool ◽  
Hard Exudates ◽  
Adaptive Histogram Equalization ◽  
Cotton Wool Spots ◽  
Enhancement Method

<div>The automatic retinal disease diagnosis by artificial intelligent is an interesting and challenging topic in the medical field. It requires an appropriate image enhancement technique and a sufficient training dataset for the specific retina conditions. The aim of this study was to design an automatic diagnosis convolutional neural network (CNN) model which does not require a large training dataset to specifically identify diabetic retinopathy symptoms, which are cotton wool, exudates spots and red lesionin colour fundus pictures. A novel framework comprised image enhancement method by using upgraded contrast limited adaptive histogram equalization (UCLAHE) filter and transferred pre-trained networks was developed to classify the retinal diseases regarding to the symptoms. The performance of the proposed framework was evaluated based on accuracy, sensitivity and specificity metrics. The collected results have proven the robustness of the proposed framework in offering good accuracy in retina diseases diagnosis. </div>

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

2021 ◽  
Vol 5 (6) ◽  
pp. 1062-1069
Author(s):  
Shoffan Saifullah ◽  
Andiko Putro Suryotomo ◽  
Yuhefizar
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Detection Methods ◽  
Adaptive Histogram Equalization ◽  
Chicken Egg ◽  
Enhancement Method ◽  
Edge Detection Method

This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values ​​of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.

scholarly journals Low-Light Image Enhancement Based on Quasi-Symmetric Correction Functions by Fusion

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1561
Author(s):  
Changli Li ◽  
Shiqiang Tang ◽  
Jingwen Yan ◽  
Teng Zhou
Keyword(s):  
Image Enhancement ◽  
State Of The Art ◽  
Histogram Equalization ◽  
Local Contrast ◽  
Low Light ◽  
Adaptive Histogram Equalization ◽  
Light Image ◽  
Image Capturing ◽  
Enhancement Method ◽  
Image Details

Sometimes it is very difficult to obtain high-quality images because of the limitations of image-capturing devices and the environment. Gamma correction (GC) is widely used for image enhancement. However, traditional GC perhaps cannot preserve image details and may even reduce local contrast within high-illuminance regions. Therefore, we first define two couples of quasi-symmetric correction functions (QCFs) to solve these problems. Moreover, we propose a novel low-light image enhancement method based on proposed QCFs by fusion, which combines a globally-enhanced image by QCFs and a locally-enhanced image by contrast-limited adaptive histogram equalization (CLAHE). A large number of experimental results showed that our method could significantly enhance the detail and improve the contrast of low-light images. Our method also has a better performance than other state-of-the-art methods in both subjective and objective assessments.

scholarly journals Contrast Enhancement of MRI Images using AHE and CLAHE Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 2442-2445 ◽  
Keyword(s):  
Image Processing ◽  
Contrast Enhancement ◽  
Image Enhancement ◽  
Medical Image ◽  
Histogram Equalization ◽  
Mri Scan ◽  
Adaptive Histogram Equalization ◽  
Shadowing Effect ◽  
Suitable Technique

Medical images require image enhancement, a category of image processing which provides better visualization that make diagnostic more accurate. The most commonly used method for improving the quality of medical image is Contrast enhancement.The main objective is to eliminate the use of contrast dye during the process of MRI scan and to find the parameters MSE, PSNR, AMBE and contrast and compare the result. The histogram equalization (HE) is the widely accepted method which is not productive when the contrast nature differs across the image. Adaptive Histogram Equalization (AHE) overcomes this limitation by considering and developing the mapping for each pixel from the histogram in a neighboring window. Another suitable technique is CLAHE. CLAHE is a refinement of AHE where the enhancement calculation is modified by imposing a user specified level to the height of local histogram. The enhancement is thereby reduced in very uniform areas of the image, which prevents over enhancement of noise and reduces the edge shadowing effect of unlimited AHE. After enhancing the image using AHE and CLAHE the comparison of their parameters is performed.

scholarly journals PERBAIKAN CITRA INFRA MERAH DENGAN METODE CELLULAR AUTOMATA

2018 ◽  
Vol 18 (2) ◽  
pp. 61
Author(s):  
Annisa Yuniar Hidayah ◽  
Abduh Riski ◽  
Ahmad Kamsyakawuni
Keyword(s):  
Cellular Automata ◽  
Image Enhancement ◽  
Infrared Image ◽  
Histogram Equalization ◽  
Initial State ◽  
Low Contrast ◽  
Linear Index ◽  
Infrared Cameras ◽  
Image Detail

Image enhancement is needed because not all images have good quality, such as noise, too low contrast or blurry image. These problems are commonly found in images generated from infrared cameras, therefore this study uses infrared imagery as an image to be corrected. The method that will be used to improve the image, namely Cellular Automata method. The edge detection using the Prewitt operator will be used as the initial state of Cellular Automata cells. The results obtained from this research is Cellular Automata method can improve the quality of infrared image well. Visually, the Cellular Automata method successfully improves image contrast and retains the infrared image detail so as not to reduce the value of information from the image. Calculated using the Linear Index of Fuzziness, the results of the Cellular Automata method are better only on some imagery only when compared to the Histogram Equalization mode. Keywords: Infrared Image, Image Enhancement, Cellular Automata

Sign in / Sign up

Export Citation Format

Share Document