压缩感知图像恢复的感知评价

Bo Hu, Leida Li, Jiansheng Qian, Yuming Fang
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引用次数: 4

摘要

近年来,压缩感知技术受到了广泛的关注。广泛的CS恢复算法已经提出了有效的图像重建。然而,CS图像恢复算法和相应恢复图像的感知评价方面的工作很少。本文首先构建了压缩感知恢复图像数据库(CSRID),该数据库包含了十种流行的压缩感知图像恢复算法在不同感知速率下生成的图像。然后采用单刺激法进行主观实验,获得图像的主观品质。然后用主观分数来评价CS图像恢复算法的性能。最后,研究了通用无参考(NR)质量指标和图像模糊指标在CSRID数据库上的性能。实验结果表明,最先进的质量指标在预测CS恢复图像质量方面是非常有限的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Perceptual evaluation of Compressive Sensing Image Recovery
Compressive sensing (CS) has been attracting tremendous attention in recent years. Extensive CS recovery algorithms have been proposed for effective image reconstruction. However, little work has been dedicated to the perceptual evaluation of CS image recovery algorithms and the corresponding recovered images. In this paper, we first build a Compressive Sensing Recovered Image Database (CSRID), which contains images generated by ten popular CS image recovery algorithms at different sensing rates. We then carry out a subjective experiment using the single-stimulus method to obtain the subjective qualities of the images. The subjective scores are then used to evaluate the performances of the CS image recovery algorithms. Finally, the performances of general-purpose no-reference (NR) quality metrics and image blur metrics are investigated on the CSRID database. Experimental results show that the state-of-the-art quality metrics are very limited in predicting the quality of CS recovered images.
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