Image Contrast Enhancement Algorithm Based on GM(1,1) and Power Exponential Dynamic Decision

2017 ◽  
Vol 32 (02) ◽  
pp. 1854002 ◽  
Author(s):  
Gang Li
Keyword(s):  
Image Enhancement ◽  
Local Contrast ◽  
Local Enhancement ◽  
Contrast Level ◽  
Image Edge Detection ◽  
Image Contrast Enhancement ◽  
Image Edge ◽  
Image Enhancing ◽  
Enhancement Algorithm

Image enhancement processing is a very important operation during image preprocessing. Compared with to enhancc the overall contrast level of image, enhancing the local contrast of image can improve the level of such contrast directly as well as the quality and effect of image enhancement. In this paper, the gray prediction model is applied to the process of enhancing image local contrast, so as to measure the change range of image local contrast and adaptively adjust the scale of enhancing image local contrast. The simulation results show that, in addition to enhancing the contrast of gray level on the edge of image, the proposed algorithm can inhibit roughened nonedge region and improve the quality of local enhancement processing, which create a more favorable condition for the further image edge detection.

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.

Coal Gangue Image Edge Detection Based on Wavelet Transform

2012 ◽  
Vol 214 ◽  
pp. 375-380 ◽  
Author(s):  
Tie Yun Li
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Detection Algorithm ◽  
Coal Gangue ◽  
Edge Detection Algorithm ◽  
Image Edge Detection ◽  
Multi Resolution Analysis ◽  
Resolution Analysis ◽  
Image Edge

An edge detection algorithm is developed for coal gangue images, and the method has two advantages compared with traditional ones. Firstly, multi-resolution analysis of wavelet transform can improve the quality of edge detection. Secondly, the algorithm is faster for real time. Since the threshold directly from the coefficients of wavelet transform, the rate of recognition for coal gangue is highly raised. The experiment results show that the method is an efficient edge detection algorithm for extraction edges from the noised images of coal gangues.

scholarly journals Algorithmic Approaches For Image Edge Detection

2021 ◽  
Vol 10 (3) ◽  
pp. 1942-1948
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Image Enhancement ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Edge Detection ◽  
Image Edge ◽  
Algorithmic Approaches

Edge detection is most important technique in digital image processing. It play an important role in image segmentation and many other applications. Edge detection providesfoundation to many medical and military applications.It difficult to generate a generic code for edge detection so many kinds ofalgorithms are available. In this article 4 different approaches Global image enhancement with addition (GIEA), Global image enhancement with Multiplication (GIEM),Without Global image enhancement with Addition (WOGIEA),and without Global image enhancement with Multiplication (WOGIEM)for edge detection is proposed. These algorithms are validatedon 9 different images. The results showthat GIEA give us more accurate results as compare to other techniques.

scholarly journals Image Enhancement by Frequency Analysis

2018 ◽  
Vol 228 ◽  
pp. 02008
Author(s):  
Chen Yao ◽  
Yan Xia ◽  
Jiamin Zhu
Keyword(s):  
Image Enhancement ◽  
Frequency Analysis ◽  
Linear Mapping ◽  
Experimental Results ◽  
Image Mapping ◽  
Enhancement Factors ◽  
Central Energy ◽  
Enhancement Algorithm

Because of lighting or the quality of CMOS/CCD, poor images are often gained, which greatly affect subjective observation. Image enhancement can improve the contrast of poor image. In our paper, we propose a new image enhancement algorithm based on frequency analysis. A central energy of FFT is utilized for computation of image enhancement factors. A linear mapping is used for image mapping. Finally, some experimental results are shown for illustration of our algorithm advantage.

An Improved Adaptive Unsharp Masking Image Enhancement Algorithm

2013 ◽  
Vol 325-326 ◽  
pp. 1547-1550
Author(s):  
Li Zhu ◽  
Chun Qiang Zhu
Keyword(s):  
Contrast Enhancement ◽  
Real Time ◽  
Image Enhancement ◽  
Digital Tv ◽  
Image Sharpening ◽  
Real Time Processing ◽  
Time Processing ◽  
Unsharp Masking ◽  
Image Contrast Enhancement ◽  
Enhancement Algorithm

When needing a reliable adaptive image contrast enhancement in real-time processing such as digital TV postprocessing,This goal is achieved by an improved adaptive unsharp masking image enhancement algorithm in the paper. The proposed improved adaptive unsharp masking filter controls the contribution of the sharpening path by the output of Laplacian and works in such a way that contrast enhancement occurs in high detail areas and little or no image sharpening occurs in smooth areas. The experiment shows that this improved algorithm greatly reduce the complexity of computing and can be reliably used.

scholarly journals An Image Enhancement Algorithm Based on Fractional-Order Phase Stretch Transform and Relative Total Variation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wei Wang ◽  
Ying Jia ◽  
Qiming Wang ◽  
Pengfei Xu
Keyword(s):  
Image Enhancement ◽  
Total Variation ◽  
Fractional Order ◽  
Complex Environments ◽  
Low Contrast ◽  
Low Pass ◽  
Phase Stretch Transform ◽  
Enhancement Algorithm ◽  
Relative Total Variation

The main purpose of image enhancement technology is to improve the quality of the image to better assist those activities of daily life that are widely dependent on it like healthcare, industries, education, and surveillance. Due to the influence of complex environments, there are risks of insufficient detail and low contrast in some images. Existing enhancement algorithms are prone to overexposure and improper detail processing. This paper attempts to improve the treatment effect of Phase Stretch Transform (PST) on the information of low and medium frequencies. For this purpose, an image enhancement algorithm on the basis of fractional-order PST and relative total variation (FOPSTRTV) is developed to address the task. In this algorithm, the noise in the original image is removed by low-pass filtering, the edges of images are extracted by fractional-order PST, and then the images are fused with extracted edges through RTV. Finally, extensive experiments were used to verify the effect of the proposed algorithm with different datasets.

scholarly journals Optimal Method of Vision Sensor Digital Image Edge Detection for Commercial Investment in Film Field

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zheng Liu
Keyword(s):  
Edge Detection ◽  
Digital Image ◽  
Film Industry ◽  
Rapid Development ◽  
Visual Sensor ◽  
Optimal Method ◽  
Sound Effects ◽  
Image Edge Detection ◽  
Image Edge

With the rapid development of the national economy, the film industry has risen rapidly under these conditions, and the development of film occupies a more important position in this process, which has led to the development of investment in the film field, an internationally recognized cultural space. This paper studies the commercial investment in the film field based on the edge detection of the digital image of the visual sensor. The purpose of the research is to point out the direction of commercial investment in the film industry by studying the effect of the edge detection of the digital image of the visual sensor on the application of the film object. The image edge detection algorithm based on wavelet transform and morphology and the application of these image edge detection algorithms such as wavelet theory analyze the shortcomings of movie, sound effects, and pictures and make a comparison before and after the improvement. Audiences, as the direct enjoyers of the cultural product of the film, their opinions, and evaluations of the film, largely determine the quality of the work. On the contrary, a good work can attract the public’s attention and bring a large impact. The data acquisition in this process is mainly based on the questionnaire survey of the audience. The experimental results show that before applying digital image edge detection to transform the clip, 7 people thought that the clip needed to be further improved to improve the quality of the film itself, and only 1 person affirmed the film; after the modification, 9 people proposed the film praise; in addition, 15 people audience watched the film processed by the edge detection method and gave high evaluations to the three aspects of sound effects, special effects, and editing, especially the editing part, with a score of 8.9.

scholarly journals Classical and Fuzzy Based Image Enhancement Techniques for Banana Root Disease Diagnosis: A Review and Validation

2020 ◽  
Vol 13 (1) ◽  
pp. 50-62
Author(s):  
D. Suryaprabha ◽  
J. Satheeshkumar ◽  
N. Seenivasan
Keyword(s):  
Image Quality ◽  
Frequency Domain ◽  
Image Enhancement ◽  
Plant Root ◽  
Histogram Equalization ◽  
Disease Diagnosis ◽  
Root Disease ◽  
Classical Point ◽  
Enhancement Algorithm

A vital step in automation of plant root disease diagnosis is to extract root region from the input images in an automatic and consistent manner. However, performance of segmentation algorithm over root images directly depends on the quality of input images. During acquisition, the captured root images are distorted by numerous external factors like lighting conditions, dust and so on. Hence it is essential to incorporate an image enhancement algorithm as a pre-processing step in the plant root disease diagnosis module. Image quality can be improved either by manipulating the pixels through spatial or frequency domain. In spatial domain, images are directly manipulated using their pixel values and alternatively in frequency domain, images are indirectly manipulated using transformations. Spatial based enhancement methods are considered as favourable approach for real time root images as it is simple and easy to understand with low computational complexity. In this study, real time banana root images were enhanced by attempting with different spatial based image enhancement techniques. Different classical point processing methods (contrast stretching, logarithmic transformation, power law transformation, histogram equalization, adaptive histogram equalization and histogram matching) and fuzzy based enhancement methods using fuzzy intensification operator and fuzzy if-then rule based methods were tried to enhance the banana root images. Quality of the enhanced root images obtained through different classical point processing and fuzzy based methods were measured using no-reference image quality metrics, entropy and blind image quality index. Hence, this study concludes that fuzzy based method could be deployed as a suitable image enhancement algorithm while devising the image processing modules for banana root disease diagnosis.

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