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.

CONTRAST ENHANCEMENT USING TEXTURE HISTOGRAM AND FUZZY ENTROPY

2007 ◽  
Vol 03 (03) ◽  
pp. 349-365
Author(s):  
YANHUI GUO ◽  
H. D. CHENG ◽  
JIANHUA HUANG ◽  
WEI ZHAO ◽  
XIANGLONG TANG
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Fuzzy Entropy ◽  
Experimental Results ◽  
Noise Amplification ◽  
Enhancement Algorithm

Image enhancement is used to correct contrast deficiencies and to improve the quality of an image. It is essential and critical to extracting features and segmenting images. This paper presents a novel contrast enhancement algorithm based on newly defined texture histogram and fuzzy entropy with the ability to preserve edges and details, while avoiding noise amplification and over-enhancement. To demonstrate the performance, the proposed algorithm is tested on a variety of images and compared with other enhancement algorithms. Experimental results proved that the proposed method has better performance in enhancing images without over-enhancement and under-enhancement.

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 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 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.

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.

scholarly journals Modified Local and Global Contrast Enhancement Algorithm for Color Satellite Image

2019 ◽  
Vol 224 ◽  
pp. 04010
Author(s):  
Viacheslav Voronin
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Satellite Images ◽  
Color Image ◽  
State Of The Art ◽  
Satellite Image ◽  
Detailed Model ◽  
Global Contrast ◽  
Enhancement Algorithm

The quality of remotely sensed satellite images depends on the reflected electromagnetic radiation from the earth’s surface features. Lack of consistent and similar amounts of energy reflected by different features from the earth’s surface results in a poor contrast satellite image. Image enhancement is the image processing of improving the quality that the results are more suitable for display or further image analysis. In this paper, we present a detailed model for color image enhancement using the quaternion framework. We introduce a novel quaternionic frequency enhancement algorithm that can combine the color channels and the local and global image processing. The basic idea is to apply the α-rooting image enhancement approach for different image blocks. For this purpose, we split image in moving windows on disjoint blocks. The parameter alfa for every block and the weights for every local and global enhanced image driven through optimization of measure of enhancement (EMEC). Some presented experimental results illustrate the performance of the proposed approach on color satellite images in comparison with the state-of-the-art methods.

scholarly journals An Underwater Image Enhancement Algorithm Based on MSR Parameter Optimization

2020 ◽  
Vol 8 (10) ◽  
pp. 741
Author(s):  
Kai Hu ◽  
Yanwen Zhang ◽  
Feiyu Lu ◽  
Zhiliang Deng ◽  
Yunping Liu
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Environmental Changes ◽  
Gravitational Search Algorithm ◽  
Reference Image ◽  
Natural Image ◽  
Enhancement Effect ◽  
Underwater Image ◽  
Enhancement Algorithm

The quality of underwater images is often affected by the absorption of light and the scattering and diffusion of floating objects. Therefore, underwater image enhancement algorithms have been widely studied. In this area, algorithms based on Multi-Scale Retinex (MSR) represent an important research direction. Although the visual quality of underwater images can be improved to some extent, the enhancement effect is not good due to the fact that the parameters of these algorithms cannot adapt to different underwater environments. To solve this problem, based on classical MSR, we propose an underwater image enhancement optimization (MSR-PO) algorithm which uses the non-reference image quality assessment (NR-IQA) index as the optimization index. First of all, in a large number of experiments, we choose the Natural Image Quality Evaluator (NIQE) as the NR-IQA index and determine the appropriate parameters in MSR as the optimization object. Then, we use the Gravitational Search Algorithm (GSA) to optimize the underwater image enhancement algorithm based on MSR and the NIQE index. The experimental results show that this algorithm has an excellent adaptive ability to environmental changes.

scholarly journals Application of Histogram Equalization for Image Enhancement in Corrosion Areas

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jianbin Xiong ◽  
Dezheng Yu ◽  
Qi Wang ◽  
Lei Shu ◽  
Jian Cen ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Histogram Equalization ◽  
Experimental Results ◽  
Blue Color ◽  
Low Contrast ◽  
Saturation Intensity ◽  
Enhancement Algorithm ◽  
Intensity Model

In this paper, an image enhancement algorithm is presented for identification of corrosion areas and dealing with low contrast present in shadow areas of an image. This algorithm uses histogram equalization processing under the hue-saturation-intensity model. First of all, an etched image is transformed from red-green-blue color space to hue-saturation-intensity color space, and only the luminance component is enhanced. Then, part of the enhanced image is combined with the original tone component, followed by saturation and conversion to red-green-blue color space to obtain the enhanced corrosion image. Experimental results show that the proposed method significantly improves overall brightness, increases contrast details in shadow areas, and strengthens identification of corrosion areas in the image.

scholarly journals Hue-correction scheme considering CIEDE2000 for color-image enhancement including deep-learning-based algorithms

2020 ◽  
Vol 9 ◽  
Author(s):  
Yuma Kinoshita ◽  
Hitoshi Kiya
Keyword(s):  
Deep Learning ◽  
Image Enhancement ◽  
Color Image ◽  
Mapping Method ◽  
Experimental Results ◽  
Color Gamut ◽  
Gamut Mapping ◽  
Color Image Enhancement ◽  
Correction Scheme ◽  
Enhancement Algorithm

In this paper, we propose a novel hue-correction scheme for color-image-enhancement algorithms including deep-learning-based ones. Although hue-correction schemes for color-image enhancement have already been proposed, there are no schemes that can both perfectly remove perceptual hue-distortion on the basis of CIEDE2000 and be applicable to any image-enhancement algorithms. In contrast, the proposed scheme can perfectly remove hue distortion caused by any image-enhancement algorithm such as deep-learning-based ones on the basis of CIEDE2000. Furthermore, the use of a gamut-mapping method in the proposed scheme enables us to compress a color gamut into an output RGB color gamut, without hue changes. Experimental results show that the proposed scheme can completely correct hue distortion caused by image-enhancement algorithms while maintaining the performance of the algorithms and ensuring the color gamut of output images.

scholarly journals Wavelets assisted fuzzy edge refinement

2014 ◽  
Vol 14 (2) ◽  
pp. 5409-5418
Author(s):  
Kumud Saxena
Keyword(s):  
Feature Extraction ◽  
Object Recognition ◽  
Edge Detection ◽  
Image Enhancement ◽  
Experimental Results ◽  
Edge Enhancement ◽  
Processing Step ◽  
Edge Detectors ◽  
Novel Algorithm

Image enhancement is a crucial pre-processing step to be performed for various applications where object recognition, identification, verification is required. Among various image enhancement methods, edge enhancement has taken its importance as it is widely used for understanding features in an image. Several types of edge detectors are available for certain types of edges. If edges are enhanced and clear, the reliability for feature extraction increases. The Quality of edge detection can be measured from several criteria objectively. In this paper, a novel algorithm for edge enhancement has been proposed for multiple types of images. The features can be extracted clearly by using this method. For comparison purpose Roberts, Sobel, Prewitt, Canny, and Log edge operators are used and their results are displayed. Experimental results demonstrate the effectiveness of the proposed approach.

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