针对红外图像增强过程中容易饱和、细节丢失等问题,提出一种参数自设定的双直方图均衡化方法。根据灰度级累积概率密度黄金比例值将原始图像划分为两个独立的子图像。结合原始图像曝光度和子图像灰度级区间信息,对每个子图像的直方图进...针对红外图像增强过程中容易饱和、细节丢失等问题,提出一种参数自设定的双直方图均衡化方法。根据灰度级累积概率密度黄金比例值将原始图像划分为两个独立的子图像。结合原始图像曝光度和子图像灰度级区间信息,对每个子图像的直方图进行多尺度自适应加权校正。基于校正后的直方图,对每个子图像分别作均衡化映射变换,最后合并子图像获得增强图像。在红外图像公开数据集INFRARED100上进行的测试显示,与亮度保持双直方图均衡化(Brightness Preserving Bi-Histogram Equalization,BBHE)、带平台限制的双直方图均衡化(Bi-histogram Equalization with a Plateau Limit,BHEPL)、基于曝光度的双直方图均衡化(Exposure based Sub-image Histogram Equalization,ESIHE)方法相比,所提方法增强的图像具有合适的平均对比度和更大的平均信息熵,在峰值信噪比(Peak Signal-to-Noise Ratio,PSNR)、结构相似度(Structural Similarity,SSIM)、绝对平均亮度偏差(Absolute Mean Brightness Error,AMBE)指标上平均提升至少17.2%、4.0%、56.2%。实验结果表明,所提方法对不同亮度特征的红外图像都有良好的适应性,可有效增强红外图像对象和背景之间的对比度,在噪声抑制、亮度和细节保持等方面优于同类方法。展开更多
Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic pro...Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast accuracy.The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups.In light of research investigations,it is vital to consider age as one of the key criteria when choosing the subjects.The younger subjects are more susceptible to the perishable side than the older onset.The proposed investigation concentrated on the younger onset.The research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages automatically.The proposed work is executed in three steps.The 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)methods.The Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of AD.The model was trained and tested to classify the five stages of AD.The ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.展开更多
文摘针对红外图像增强过程中容易饱和、细节丢失等问题,提出一种参数自设定的双直方图均衡化方法。根据灰度级累积概率密度黄金比例值将原始图像划分为两个独立的子图像。结合原始图像曝光度和子图像灰度级区间信息,对每个子图像的直方图进行多尺度自适应加权校正。基于校正后的直方图,对每个子图像分别作均衡化映射变换,最后合并子图像获得增强图像。在红外图像公开数据集INFRARED100上进行的测试显示,与亮度保持双直方图均衡化(Brightness Preserving Bi-Histogram Equalization,BBHE)、带平台限制的双直方图均衡化(Bi-histogram Equalization with a Plateau Limit,BHEPL)、基于曝光度的双直方图均衡化(Exposure based Sub-image Histogram Equalization,ESIHE)方法相比,所提方法增强的图像具有合适的平均对比度和更大的平均信息熵,在峰值信噪比(Peak Signal-to-Noise Ratio,PSNR)、结构相似度(Structural Similarity,SSIM)、绝对平均亮度偏差(Absolute Mean Brightness Error,AMBE)指标上平均提升至少17.2%、4.0%、56.2%。实验结果表明,所提方法对不同亮度特征的红外图像都有良好的适应性,可有效增强红外图像对象和背景之间的对比度,在噪声抑制、亮度和细节保持等方面优于同类方法。
文摘Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast accuracy.The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups.In light of research investigations,it is vital to consider age as one of the key criteria when choosing the subjects.The younger subjects are more susceptible to the perishable side than the older onset.The proposed investigation concentrated on the younger onset.The research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages automatically.The proposed work is executed in three steps.The 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)methods.The Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of AD.The model was trained and tested to classify the five stages of AD.The ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.
