中文摘要：

引用了一种新的L1／L2的正则项形式，这种形式对清晰图像分布状况的描述比传统范数形式更好．本文创新之处在于提出一种加入不等式约束的模糊核估计的优化更新算法，该算法通过保持卷积核的支撑区域并消除周围噪声的方法使得对于模糊卷积核的估计变得更加精确，最终达到针对运动模糊图像的更好的复原效果．

英文摘要：

A new type of L1/L2 regularization term is proposed that yields a better description of sharp image distri- bution than the traditional norms. In addition, we propose an optimization refinement algorithm for blur-kernel estimation with inequality constraints. The algorithm eliminates the nearby noise and keeps the support region of the convolution kernel, which offers a more accurate estimation of the blur convolution kernel. Finally, this algorithm produces a better deblurring result for motion-blurred images.