We present a highly efficient blind restoration method to remove mild blur in natural images. Contrary to the mainstream, we focus on removing slight blur that is often present, damaging image quality and commonly generated by small out-of-focus, lens blur, or slight camera motion. The proposed algorithm first estimates image blur and then compensates for it by combining multiple applications of the estimated blur in a principled way. To estimate blur we introduce a simple yet robust algorithm based on empirical observations about the distribution of the gradient in sharp natural images. Our experiments show that, in the context of mild blur, the proposed method outperforms traditional and modern blind deblurring methods and runs in a fraction of the time. Our method can be used to blindly correct blur before applying off-the-shelf deep super-resolution methods leading to superior results than other highly complex and computationally demanding techniques. The proposed method estimates and removes mild blur from a 12MP image on a modern mobile phone in a fraction of a second.

该研究提出了一种高效的盲恢复方法，利用基于梯度分布的图像模糊估计算法并结合多次应用来去除自然图像中的轻微模糊，取得了比传统和现代盲去模糊方法更好的效果，可以在现代手机上以秒为单位的时间内对12MP图像进行模糊校正，为应用深度超分辨率技术提供了更优的图像处理前处理。

Polyblur：通过多项式再模糊，去除轻微模糊