Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algori...Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algorithm to achieve optimal results.In our proposed model,gamma correction and Retinex address color cast issues and enhance image edges and details.The final enhanced image is obtained through color balancing.The BES algorithm seeks the optimal solution through the selection,search,and swooping stages.However,it is prone to getting stuck in local optima and converges slowly.To overcome these limitations,we propose an improved BES algorithm(ABES)with enhanced population learning,position updates,and control parameters.ABES is employed to optimize the core parameters of gamma correction and Retinex to improve image quality,and the maximization of information entropy is utilized as the objective function.Real benchmark images are collected to validate its performance.Experimental results demonstrate that ABES outperforms the existing image enhancement methods,including the flower pollination algorithm,the chimp optimization algorithm,particle swarm optimization,and BES,in terms of information entropy,peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and patch-based contrast quality index(PCQI).ABES demonstrates superior performance both qualitatively and quantitatively,and it helps enhance prominent features and contrast in the images while maintaining the natural appearance of the original images.展开更多
In order to improve image quality, a novel Retinex algorithm for image enhancement was presented. Different from conventional algorithms, it was based on certain defined points containing the illumination information ...In order to improve image quality, a novel Retinex algorithm for image enhancement was presented. Different from conventional algorithms, it was based on certain defined points containing the illumination information in the intensity image to estimate the illumination. After locating the points, the whole illumination image was computed by an interpolation technique. When attempting to recover the reflectance image, an adaptive method which can be considered as an optimization problem was employed to suppress noise in dark environments and keep details in other areas. For color images, it was taken in the band of each channel separately. Experimental results demonstrate that the proposed algorithm is superior to the traditional Retinex algorithms in image entropy.展开更多
为均衡增强低照度图像的同时,保留其更多的细节信息,提出一种改进Retinex低照度图像增强算法.该算法基于HSV(Hue,Saturation,Value)颜色空间,对分离出的明度分量和饱和度分量进行增强.首先,使用限制对比度自适应直方图均衡化(Contrast L...为均衡增强低照度图像的同时,保留其更多的细节信息,提出一种改进Retinex低照度图像增强算法.该算法基于HSV(Hue,Saturation,Value)颜色空间,对分离出的明度分量和饱和度分量进行增强.首先,使用限制对比度自适应直方图均衡化(Contrast Limited Adap-tive Histogram Equalization,CLAHE)优化明度分量,使图像更接近均匀光照场景,并使用自适应Gamma对饱和度分量进行校正.然后,采用三维块匹配滤波(Block-matching and 3D Filter-ing,BM3D)算法对光照分量进行估计,并求得相应的反射分量,提出一种改进Gamma变换函数,依据光照分量信息对明度分量进行增强,同时,采用Gabor滤波器和Canny算法对原图进行细节提取,提出一种细节增强策略,对反射分量及其纹理细节进行增强.最后,将各分量进行加权融合,再将增强图像变换回RGB空间.实验结果表明,所提算法相较于自动色彩均衡、自适应局部色调映射、低光照图像增强、带色彩恢复多尺度视网膜增强算法有更好的增强效果和普适性,且原图经过增强后,信息熵、峰值信噪比、结构相似性指数、图像质量指数、平均梯度有显著提升,均方根误差显著下降.展开更多
基金supported by the Research on theKey Technology of Damage Identification Method of Dam Concrete Structure based on Transformer Image Processing(242102521031)the project Research on Situational Awareness and Behavior Anomaly Prediction of Social Media Based on Multimodal Time Series Graph(232102520004)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B520019).
文摘Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algorithm to achieve optimal results.In our proposed model,gamma correction and Retinex address color cast issues and enhance image edges and details.The final enhanced image is obtained through color balancing.The BES algorithm seeks the optimal solution through the selection,search,and swooping stages.However,it is prone to getting stuck in local optima and converges slowly.To overcome these limitations,we propose an improved BES algorithm(ABES)with enhanced population learning,position updates,and control parameters.ABES is employed to optimize the core parameters of gamma correction and Retinex to improve image quality,and the maximization of information entropy is utilized as the objective function.Real benchmark images are collected to validate its performance.Experimental results demonstrate that ABES outperforms the existing image enhancement methods,including the flower pollination algorithm,the chimp optimization algorithm,particle swarm optimization,and BES,in terms of information entropy,peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and patch-based contrast quality index(PCQI).ABES demonstrates superior performance both qualitatively and quantitatively,and it helps enhance prominent features and contrast in the images while maintaining the natural appearance of the original images.
基金Project(61071162) supported by the National Natural Science Foundation of China
文摘In order to improve image quality, a novel Retinex algorithm for image enhancement was presented. Different from conventional algorithms, it was based on certain defined points containing the illumination information in the intensity image to estimate the illumination. After locating the points, the whole illumination image was computed by an interpolation technique. When attempting to recover the reflectance image, an adaptive method which can be considered as an optimization problem was employed to suppress noise in dark environments and keep details in other areas. For color images, it was taken in the band of each channel separately. Experimental results demonstrate that the proposed algorithm is superior to the traditional Retinex algorithms in image entropy.
文摘为均衡增强低照度图像的同时,保留其更多的细节信息,提出一种改进Retinex低照度图像增强算法.该算法基于HSV(Hue,Saturation,Value)颜色空间,对分离出的明度分量和饱和度分量进行增强.首先,使用限制对比度自适应直方图均衡化(Contrast Limited Adap-tive Histogram Equalization,CLAHE)优化明度分量,使图像更接近均匀光照场景,并使用自适应Gamma对饱和度分量进行校正.然后,采用三维块匹配滤波(Block-matching and 3D Filter-ing,BM3D)算法对光照分量进行估计,并求得相应的反射分量,提出一种改进Gamma变换函数,依据光照分量信息对明度分量进行增强,同时,采用Gabor滤波器和Canny算法对原图进行细节提取,提出一种细节增强策略,对反射分量及其纹理细节进行增强.最后,将各分量进行加权融合,再将增强图像变换回RGB空间.实验结果表明,所提算法相较于自动色彩均衡、自适应局部色调映射、低光照图像增强、带色彩恢复多尺度视网膜增强算法有更好的增强效果和普适性,且原图经过增强后,信息熵、峰值信噪比、结构相似性指数、图像质量指数、平均梯度有显著提升,均方根误差显著下降.
