  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
