scholarly journals Video Enhancement using Histogram Equalization with JND Model

2019 ◽  
Vol 8 (2) ◽  
pp. 2506-2511
Keyword(s):  
Visual System ◽  
Contrast Enhancement ◽  
Histogram Equalization ◽  
Video Frame ◽  
Video Enhancement ◽  
Difference Model ◽  
Face Region ◽  
Camera Phones ◽  
Area Of Interest ◽  
The Face

The paper presents degraded Video contrast enhancement. These videosare taken by camera phones because of the improper illumination or limitation of the capturing devices. The existing enhancement approach may either flop to produce good and distortionless Videos. They do not enhance every area of interest properly, especially in face regions. The paperpropose histogram equalization method (HE) manipulating thenoticeable in difference model of the visual system represented as in JND-HE. This will be performed for generic frame that is contrast enhancement. In addition, the said method,JND-HE method is clubbed with the exposer correctivemethod represented by JND-HE-EC for video enhancement of face region. The EC method is to adjust the illumination of the video frame in the face region and obtain suitable illumination in the background. The demonstration result shows that generic videos and faces shallproduce pleasantvideosother than existing techniques.

scholarly journals Contrast Enhancement Technique using Discrete Wavelet Transform with Just Noticeable Difference Model for 3D Stereoscopic Degraded Video

2021 ◽  
Vol 10 (3) ◽  
pp. 7-13
Author(s):  
Bhagya H K ◽  
Keshaveni N
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Contrast Enhancement ◽  
High Frequency ◽  
Just Noticeable Difference ◽  
Video Frame ◽  
Discrete Wavelet ◽  
Difference Model ◽  
Pass Filter ◽  
Enhancement Technique

The Video Technologies for Medical, cultural, and social activities prefer 3D visual data rendering and processing. So 3D videos are captured by any capturing devices, like the digital cameras are not acceptable all the time due to the lack of capturing devices or indecent illumination or due to poor weather surroundings like Low light, rain, fog, mist, etc. reduces the contrast, thus the videos get degraded. 3D video contrast enhancement technique is an essential process for upgrading the quality and information content in the videos. The proposed work employs a discrete wavelet transform based enhancement technique with Jut noticeable difference model to improve the video frames and it is simple and computationally inexpensive. The application of DWT results in the Low and High-frequency sub-bands. The low-frequency components that contain the greatest amount of the information are improved using weighted threshold histogram equalization(WTHE) with the JND model algorithm while the high-frequency sub-bands are distortions and highly affected by noise. The Gaussian high pass filter is applied to each high-frequency sub-bands to remove the noise. Besides, enhancement gain control and luminance preservation are used to acquire the enhanced output video. At the end check the quality of the degraded video frame, the presented work is implemented in MATLAB 2018a and evaluated using objective parameters. Experimental results show that the proposed method can generate better and agreeable results than 2D videos.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

Face detection in single and multiple images using different skin color models

2020 ◽  
Vol 13 ◽  
Author(s):  
Manpreet Kaur ◽  
Jasdev Bhatti ◽  
Mohit Kumar Kakkar ◽  
Arun Upmanyu
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Input Image ◽  
Morphological Operations ◽  
Reliability Model ◽  
Multiple Images ◽  
Face Region ◽  
Bounding Box ◽  
The Face ◽  
Precision And Accuracy

Introduction: Face Detection is used in many different steams like video conferencing, human-computer interface, in face detection, and in the database management of image. Therefore, the aim of our paper is to apply Red Green Blue ( Methods: The morphological operations are performed in the face region to a number of pixels as the proposed parameter to check either an input image contains face region or not. Canny edge detection is also used to show the boundaries of a candidate face region, in the end, the face can be shown detected by using bounding box around the face. Results: The reliability model has also been proposed for detecting the faces in single and multiple images. The results of the experiments reflect that the algorithm been proposed performs very well in each model for detecting the faces in single and multiple images and the reliability model provides the best fit by analyzing the precision and accuracy. Moreover Discussion: The calculated results show that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images. Also, the evaluated results by this paper provides the better testing strategies that helps to develop new techniques which leads to an increase in research effectiveness. Conclusion: The calculated value of all parameters is helpful for proving that the proposed algorithm has been performed very well in each model for detecting the face by using a bounding box around the face in single as well as multiple images. The precision and accuracy of all three models are analyzed through the reliability model. The comparison calculated in this paper reflects that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images.

A Re-examination of Dichoptic Tone Mapping

2021 ◽  
Vol 40 (2) ◽  
pp. 1-15
Author(s):  
Minqi Wang ◽  
Emily A. Cooper
Keyword(s):  
Virtual Reality ◽  
Visual System ◽  
Contrast Enhancement ◽  
User Studies ◽  
Objective Measures ◽  
Tone Mapping ◽  
Photographic Technique ◽  
Stereoscopic Displays ◽  
Visual Detail

Dichoptic tone mapping methods aim to leverage stereoscopic displays to increase visual detail and contrast in images and videos. These methods, which have been called both binocular tone mapping and dichoptic contrast enhancement , selectively emphasize contrast differently in the two eyes’ views. The visual system integrates these contrast differences into a unified percept, which is theorized to contain more contrast overall than each eye’s view on its own. As stereoscopic displays become increasingly common for augmented and virtual reality (AR/VR), dichoptic tone mapping is an appealing technique for imaging pipelines. We sought to examine whether a standard photographic technique, exposure bracketing, could be modified to enhance contrast similarly to dichoptic tone mapping. While assessing the efficacy of this technique with user studies, we also re-evaluated existing dichoptic tone mapping methods. Across several user studies; however, we did not find evidence that either dichoptic tone mapping or dichoptic exposures consistently increased subjective image preferences. We also did not observe improvements in subjective or objective measures of detail visibility. We did find evidence that dichoptic methods enhanced subjective 3D impressions. Here, we present these results and evaluate the potential contributions and current limitations of dichoptic methods for applications in stereoscopic displays.

Face Image Segmentation Using Color Information and Saliency Map

2011 ◽  
Vol 55-57 ◽  
pp. 77-81
Author(s):  
Hui Ming Huang ◽  
He Sheng Liu ◽  
Guo Ping Liu
Keyword(s):  
Image Segmentation ◽  
Human Skin ◽  
Skin Color ◽  
Saliency Map ◽  
Face Image ◽  
Color Information ◽  
Attention Model ◽  
Face Area ◽  
Face Region ◽  
The Face

In this paper, we proposed an efficient method to address the problem of color face image segmentation that is based on color information and saliency map. This method consists of three stages. At first, skin colored regions is detected using a Bayesian model of the human skin color. Then, we get a chroma chart that shows likelihoods of skin colors. This chroma chart is further segmented into skin region that satisfy the homogeneity property of the human skin. The third stage, visual attention model are employed to localize the face region according to the saliency map while the bottom-up approach utilizes both the intensity and color features maps from the test image. Experimental evaluation on test shows that the proposed method is capable of segmenting the face area quite effectively，at the same time, our methods shows good performance for subjects in both simple and complex backgrounds, as well as varying illumination conditions and skin color variances.

scholarly journals State model based face mask detection

2018 ◽  
Vol 7 (2.22) ◽  
pp. 35
Author(s):  
Kavitha M ◽  
Mohamed Mansoor Roomi S ◽  
K Priya ◽  
Bavithra Devi K
Keyword(s):  
Face Mask ◽  
Search Space ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
State Model ◽  
Face Region ◽  
Automatic Teller Machine ◽  
Foreground Region ◽  
The Face ◽  
Surveillance Mechanism

The Automatic Teller Machine plays an important role in the modern economic society. ATM centers are located in remote central which are at high risk due to the increasing crime rate and robbery.These ATM centers assist with surveillance techniques to provide protection. Even after installing the surveillance mechanism, the robbers fool the security system by hiding their face using mask/helmet. Henceforth, an automatic mask detection algorithm is required to, alert when the ATM is at risk. In this work, the Gaussian Mixture Model (GMM) is applied for foreground detection to extract the regions of interest (ROI) i.e. Human being. Face region is acquired from the foreground region through  the torso partitioning and applying Viola-Jones algorithm in this search space. Parts of the face such as Eye pair, Nose, and Mouth are extracted and a state model is developed to detect  mask.  

Sign in / Sign up

Export Citation Format

Share Document