Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required....Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image quality is being assessed by full reference metrics, like MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, two more full reference metrics SSIM (Structured Similarity Indexing Method) and FSIM (Feature Similarity Indexing Method) are developed with a view to compare the structural and feature similarity measures between restored and original objects on the basis of perception. This paper is mainly stressed on comparing different image quality metrics to give a comprehensive view. Experimentation with these metrics using benchmark images is performed through denoising for different noise concentrations. All metrics have given consistent results. However, from representation perspective, SSIM and FSIM are normalized, but MSE and PSNR are not;and from semantic perspective, MSE and PSNR are giving only absolute error;on the other hand, SSIM and PSNR are giving perception and saliency-based error. So, SSIM and FSIM can be treated more understandable than the MSE and PSNR.展开更多
文摘Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image quality is being assessed by full reference metrics, like MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, two more full reference metrics SSIM (Structured Similarity Indexing Method) and FSIM (Feature Similarity Indexing Method) are developed with a view to compare the structural and feature similarity measures between restored and original objects on the basis of perception. This paper is mainly stressed on comparing different image quality metrics to give a comprehensive view. Experimentation with these metrics using benchmark images is performed through denoising for different noise concentrations. All metrics have given consistent results. However, from representation perspective, SSIM and FSIM are normalized, but MSE and PSNR are not;and from semantic perspective, MSE and PSNR are giving only absolute error;on the other hand, SSIM and PSNR are giving perception and saliency-based error. So, SSIM and FSIM can be treated more understandable than the MSE and PSNR.
文摘目的探讨前置基于多模型的自适应统计迭代重建(ASiR-V)技术对胸部CT辐射剂量和图像质量的影响。方法采用GE Revolution CT对胸部仿真体模和120例胸部CT平扫患者(分为6组,每组20例)分别设定前置ASiR-V权重为0、20%、40%、60%、80%、100%进行扫描。管电压120kV,管电流采用自动毫安(Smart mA 10-500)技术,噪声指数设为11。记录胸部体模及各组患者扫描的剂量长度乘积,计算并比较各组有效剂量(ED)。以胸部体模不同组织结构(肺组织、脊柱旁软组织、主动脉和椎体)的CT值和标准差(SD)作为客观指标,结合对各组患者的图像主观评分,比较图像质量的组间差异。结果随着前置ASiR-V权重的增加,体模及患者的ED均呈对数降低,体模不同组织CT值、图像噪声均未见明显改变。ASiR-V权重为40%时纵隔窗和肺窗的主观评分开始下降;60%时纵隔窗和肺窗图像主观评分相对40%时出现明显下降(P<0.05)。ASiR-V权重为40%,ED降至ASiR-V权重为0时的57.21%。结论前置ASiR-V可以降低辐射剂量,同时又不影响图像客观指标;前置ASiR-V权重为40%时,图像仍可保证临床诊断需求,且辐射剂量明显降低,临床应用价值最高。
