scholarly journals Research on Image Enhancement Algorithm Based on Artificial Intelligence

2021 ◽  
Vol 2074 (1) ◽  
pp. 012024
Author(s):  
Jie Liu ◽  
Yuanyuan Peng
Keyword(s):  
Artificial Intelligence ◽  
Image Quality ◽  
Image Enhancement ◽  
Interpolation Method ◽  
Mapping Function ◽  
Exposure Fusion ◽  
Multi Scale ◽  
Fused Image ◽  
Artificial Intelligence Technology ◽  
Enhancement Algorithm

Abstract With the continuous development of social science and technology, people have higher and higher requirements for image quality. This paper integrates artificial intelligence technology and proposes a low-illuminance panoramic image enhancement algorithm based on simulated multi-exposure fusion. First, the image information content is used as a metric to estimate the optimal exposure rate, and the brightness mapping function is used to enhance the V component, and the low-illuminance. The image and the overexposed image are input, the medium exposure image is synthesized by the exposure interpolation method, and the low illumination image, the medium exposure image and the overexposure image are merged using a multi-scale fusion strategy to obtain the fused image, which is corrected by a multi-scale detail enhancement algorithm. After the fusion, the details are enhanced to obtain the final enhanced image. Practice has proved that the algorithm can effectively improve the image quality.

scholarly journals Contrast and Synthetic Multiexposure Fusion for Image Enhancement

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Marwan Ali Albahar
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Image Enhancement ◽  
State Of The Art ◽  
Input Image ◽  
Synthetic Image ◽  
Improve Image Quality ◽  
Exposure Fusion ◽  
Novel Technique ◽  
Lighting Conditions

Many hardware and software advancements have been made to improve image quality in smartphones, but unsuitable lighting conditions are still a significant impediment to image quality. To counter this problem, we present an image enhancement pipeline comprising synthetic multi-image exposure fusion and contrast enhancement robust to different lighting conditions. In this paper, we propose a novel technique of generating synthetic multi-exposure images by applying gamma correction to an input image using different values according to its luminosity for generating multiple intermediate images, which are then transformed into a final synthetic image by applying contrast enhancement. We observed that our proposed contrast enhancement technique focuses on specific regions of an image resulting in varying exposure, colors, and details for generating synthetic images. Visual and statistical analysis shows that our method performs better in various lighting scenarios and achieves better statistical naturalness and discrete entropy scores than state-of-the-art methods.

Improved multi-scale retinex image enhancement algorithm

2010 ◽  
Vol 30 (8) ◽  
pp. 2091-2093 ◽  
Author(s):  
Xiao-ming WANG ◽  
Chang HUANG ◽  
Quan-bin LI ◽  
Jin-gao LIU
Keyword(s):  
Image Enhancement ◽  
Multi Scale ◽  
Enhancement Algorithm

An improved infrared image enhancement algorithm based on multi-scale decomposition

2014 ◽  
Author(s):  
Honghui Zhang ◽  
Haibo Luo ◽  
Xin-rong Yu ◽  
Qing-hai Ding
Keyword(s):  
Image Enhancement ◽  
Infrared Image ◽  
Multi Scale ◽  
Enhancement Algorithm

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.

Improved Multi Scale DR Medical Image Enhancement Algorithm

2014 ◽  
Vol 530-531 ◽  
pp. 413-417
Author(s):  
Xiao Jing Sun ◽  
Ai Bin Chen
Keyword(s):  
Image Enhancement ◽  
Low Frequency ◽  
Frequency Component ◽  
Image Feature ◽  
High Frequency Component ◽  
Image Processing Algorithm ◽  
Multi Scale ◽  
Gaussian Pyramid ◽  
Layer Image ◽  
Enhancement Algorithm

The original DR image is decomposed into different scale and frequency of the band image sequence by using Laplace gaussian pyramid model methods. Using multi-scale image enhancement algorithm to enhance the High frequency component of the decomposed image, Then adjust the light of the low frequency part to make the reconstructed image illumination contrast more reasonable. The enhanced process according to different frequency layer image feature make the different gain weight for the different frequency layer image characteristics,so different frequency image layer realize respectively noise smoothing, dimensionality reduction and enhance the effect of edge character.The simulation experiments showed that this Image Processing Algorithm effect is very good.

Enhancement Algorithm of Color Fog Image Based on the Adaptive Scale and S-Cosine Curve

2014 ◽  
Vol 513-517 ◽  
pp. 3362-3367
Author(s):  
Yin Gao ◽  
Li Jun Yun ◽  
Jun Sheng Shi ◽  
Fei Yan Cheng
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Weighted Average ◽  
Objective Evaluation ◽  
Gaussian Kernel ◽  
Correction Function ◽  
Multi Scale ◽  
Cosine Curve ◽  
Reflection Component ◽  
Enhancement Algorithm

To deal with the image contrast and color fidelity details problem in the traditional Center around the Retinex image enhancement algorithms, Enhancement algorithm of color fog image based on the adaptive scale and s-cosine curve is proposed. Firstly, the image is transformed into the RGB color space. Then the each channel pixel values can be stretched the grayscale range by S-cosine curve and introduces the local correction function. It can calculate the scale of the Gaussian kernel, and then proceeds to do the Gamma correction for the estimates of the reflection component, obtains the multi-scale image by the weighted average. Afterwards, the obtained image is used to global nonlinear correction, image sharpening and smoothing, and being superimposed reflection components, achieving the image enhancement. At last, it can carry on the intensity adjustment and grayscale adjustment for the obtained image. Through the subjective observation and objective evaluation, this algorithm is better than the traditional center around Retinex algorithm and MSRCR algorithm in processing effect.

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.

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