• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>The 4th Saudi International Conference on Information Technology: Big Data Analysis 2016 >A dialectical analysis of non-reference image quality measures (IQMs) and restoration filters for single image blind deblurring
【24h】

A dialectical analysis of non-reference image quality measures (IQMs) and restoration filters for single image blind deblurring

机译：非参考图像质量度量（IQM）和用于单个图像盲去模糊的恢复滤波器的辩证分析

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Blind image deblurring relies on a good estimation of Point Spread Function (PSF) and the utilization of an effective restoration filter. Even if the PSF is estimated well, the deblurring result depends heavily on the abilities of the restoration filter to produce a good approximation of the pristine image. Blind Image Quality Measures (IQMs) that guide the deblurring algorithm are also dependent on the restored image data. This research work evaluated the performance of the restoration filters and the blind IQMs when the true PSF has been estimate dand presents the effectiveness of both for blind deblurring. Wiener, Richardson-Lucy and Total Variation deblurring filters and BRISQUE, NIQE, SSEQ, Curvelet QA (CQA) IQMs have been analysed. Simulations have been performed over a wide range of images various blurring types (Gaussian, out-of-focus and motion). The results show that TV deblurring filter in conjunction with CQA deliver a near estimate of the pristine image for the artifically blurred images. In the case of real blurred images, Wiener filter presents high quality deblurred images and both SSEQ and CQA depict high quality images.
机译：盲图像去模糊依赖于点扩散函数（PSF）的良好估计以及有效恢复滤波器的利用。即使对PSF的估计很好，去模糊的结果也很大程度上取决于恢复滤波器产生原始图像的良好近似的能力。指导去模糊算法的盲图像质量度量（IQM）也取决于恢复的图像数据。这项研究工作评估了估计真实PSF时恢复滤波器和盲IQM的性能，并展示了两者对于盲去模糊的有效性。已分析了Wiener，Richardson-Lucy和Total Variation去模糊滤波器以及BRISQUE，NIQE，SSEQ，Curvelet QA（CQA）IQM。已经对各种模糊类型（高斯，散焦和运动）的图像进行了模拟。结果表明，与CQA结合使用的电视去模糊滤波器可为人造图像提供接近原始图像的估计。在真实模糊图像的情况下，维纳滤波器呈现高质量的去模糊图像，而SSEQ和CQA均描绘了高质量图像。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号