It is hard to treat the underwater weld seam images for the reason of bad brightness, low contrast and less welding seam information, so a new enhancement algorithm is proposed here. Firstly, the high frequency compon...It is hard to treat the underwater weld seam images for the reason of bad brightness, low contrast and less welding seam information, so a new enhancement algorithm is proposed here. Firstly, the high frequency component was separated by Gaussian filter from origin image, and then it is processed by improved local contrast enhancement(LCE) algorithm to enhance the edge information. Secondly, the gamma transform with adaptive parameters was used to strengthen the image brightness, furthermore, contrast limited adaptive histogram equalization(CLAHE) algorithm was applied to enhance the image contrast. Finally, the two manipulated images were integrated together to obtain the desired image. Experiments on typical images were carried out, and evaluation results showed that this designed algorithm can effectively improve image contrast, highlight welding seam information. Moreover, the image average grey value was moderate, and the information entropy and average gradient were much higher than other algorithms.展开更多
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
基金Project was supported by the National Science Foundation of China(Grant No.51665016)。
文摘It is hard to treat the underwater weld seam images for the reason of bad brightness, low contrast and less welding seam information, so a new enhancement algorithm is proposed here. Firstly, the high frequency component was separated by Gaussian filter from origin image, and then it is processed by improved local contrast enhancement(LCE) algorithm to enhance the edge information. Secondly, the gamma transform with adaptive parameters was used to strengthen the image brightness, furthermore, contrast limited adaptive histogram equalization(CLAHE) algorithm was applied to enhance the image contrast. Finally, the two manipulated images were integrated together to obtain the desired image. Experiments on typical images were carried out, and evaluation results showed that this designed algorithm can effectively improve image contrast, highlight welding seam information. Moreover, the image average grey value was moderate, and the information entropy and average gradient were much higher than other algorithms.
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。
