Abstract
The goal of blind image deblurring is to recover a sharp image from a motion blurred one without knowing the camera motion. Current state-of-the-art methods have a remarkably good performance on images with no noise or very low noise levels. However, the noiseless assumption is not realistic considering that low light conditions are the main reason for the presence of motion blur due to requiring longer exposure times. In fact, motion blur and high to moderate noise often appear together. Most works approach this problem by first estimating the blur kernel $k$ and then deconvolving the noisy blurred image. In this work, we first show that current state-of-the-art kernel estimation methods based on the $\ell_0$ gradient prior can be adapted to handle high noise levels while keeping their efficiency. Then, we show that a fast non-blind deconvolution method can be significantly improved by first denoising the blurry image. The proposed approach yields results that are equivalent to those obtained with much more computationally demanding methods.
Abstract (translated)
盲图像去模糊的目的是在不知道相机运动的情况下，从运动模糊的图像中恢复出清晰的图像。目前最先进的方法在没有噪音或噪音非常低的图像上有着非常好的性能。然而，考虑到低光条件是由于需要更长的曝光时间而导致运动模糊的主要原因，无噪声假设并不现实。事实上，运动模糊和高到中等噪声经常同时出现。大多数作品通过首先估计模糊内核$K$然后对噪声模糊图像进行去卷积来解决这个问题。在这项工作中，我们首先展示了当前最先进的基于$ell_0$梯度先验的核估计方法，可以在保持效率的同时处理高噪声水平。然后，对模糊图像进行去噪处理，可以显著改善快速非盲反褶积方法。所提出的方法得到的结果与用计算要求更高的方法得到的结果相当。
URL
https://arxiv.org/abs/1904.09154
