Department of Electrical and Computer Engineering, University of California, San Diego, CA 92093, USA.
IEEE Trans Image Process. 2011 Aug;20(8):2352-65. doi: 10.1109/TIP.2011.2109728. Epub 2011 Jan 31.
Liquid crystal display (LCD) devices are well known for their slow responses due to the physical limitations of liquid crystals. Therefore, fast moving objects in a scene are often perceived as blurred. This effect is known as the LCD motion blur. In order to reduce LCD motion blur, an accurate LCD model and an efficient deblurring algorithm are needed. However, existing LCD motion blur models are insufficient to reflect the limitation of human-eye-tracking system. Also, the spatiotemporal equivalence in LCD motion blur models has not been proven directly in the discrete 2-D spatial domain, although it is widely used. There are three main contributions of this paper: modeling, analysis, and algorithm. First, a comprehensive LCD motion blur model is presented, in which human-eye-tracking limits are taken into consideration. Second, a complete analysis of spatiotemporal equivalence is provided and verified using real video sequences. Third, an LCD motion blur reduction algorithm is proposed. The proposed algorithm solves an l(1)-norm regularized least-squares minimization problem using a subgradient projection method. Numerical results show that the proposed algorithm gives higher peak SNR, lower temporal error, and lower spatial error than motion-compensated inverse filtering and Lucy-Richardson deconvolution algorithm, which are two state-of-the-art LCD deblurring algorithms.
液晶显示器（LCD）设备由于液晶的物理限制，其响应速度较慢，因此场景中快速移动的物体通常会显得模糊。这种效应称为 LCD 运动模糊。为了减少 LCD 运动模糊，需要一个准确的 LCD 模型和一个有效的去模糊算法。然而，现有的 LCD 运动模糊模型不足以反映人眼跟踪系统的限制。此外，尽管在广泛使用，但在离散的 2-D 空间域中，LCD 运动模糊模型中的时空等价性尚未直接证明。本文有三个主要贡献：建模、分析和算法。首先，提出了一种综合的 LCD 运动模糊模型，其中考虑了人眼跟踪的限制。其次，提供了对时空等价性的完整分析，并使用真实视频序列进行了验证。第三，提出了一种 LCD 运动模糊减少算法。该算法使用次梯度投影法求解 l(1)-范数正则化最小二乘最小化问题。数值结果表明，与两种最先进的 LCD 去模糊算法，即运动补偿逆滤波和 Lucy-Richardson 反卷积算法相比，所提出的算法在峰值信噪比、时间误差和空间误差方面都有更高的性能。
