Eliminating image blur produced by various kinds of motion has been a challenging problem. Dominant approaches rely heavily on model capacity to remove blurring by reconstructing residual from blurry observation in feature space. These practices not only prevent the capture of spatially variable motion in the real world but also ignore the tailored handling of various motions in image space. In this paper, we propose a novel real-world deblurring filtering model called the Motion-adaptive Separable Collaborative (MISC) Filter. In particular, we use a motion estimation network to capture motion information from neighborhoods, thereby adaptively estimating spatially-variant motion flow, mask, kernels, weights, and offsets to obtain the MISC Filter. The MISC Filter first aligns the motion-induced blurring patterns to the motion middle along the predicted flow direction, and then collaboratively filters the aligned image through the predicted kernels, weights, and offsets to generate the output. This design can handle more generalized and complex motion in a spatially differentiated manner. Furthermore, we analyze the relationships between the motion estimation network and the residual reconstruction network. Extensive experiments on four widely used benchmarks demonstrate that our method provides an effective solution for real-world motion blur removal and achieves state-of-the-art performance. Code is available at https://github.com/ChengxuLiu/MISCFilter

我们提出了一种名为Motion-adaptive Separable Collaborative (MISC) Filter的新型实时去模糊滤波模型，利用运动估计网络来捕捉邻域的运动信息，从而自适应地估计空间可变的运动流、掩模、卷积核、权重和偏移量，以获取MISC Filter。该方法能够以空间差异化方式处理更为广义和复杂的运动，并且通过在预测的流方向上将运动引起的模糊模式与运动中间对齐，通过预测的卷积核、权重和偏移量协同过滤对齐的图像以生成输出。经过四个广泛使用的基准测试的大量实验表明，我们的方法为实时世界运动模糊去除提供了有效的解决方案，并达到了最先进的性能。

适应运动分离的协同滤波器用于盲运动去模糊