As recent advances in mobile camera technology have enabled the capability to capture high-resolution images, such as 4K images, the demand for an efficient deblurring model handling large motion has increased. In this paper, we discover that the image residual errors, i.e., blur-sharp pixel differences, can be grouped into some categories according to their motion blur type and how complex their neighboring pixels are. Inspired by this, we decompose the deblurring (regression) task into blur pixel discretization (pixel-level blur classification) and discrete-to-continuous conversion (regression with blur class map) tasks. Specifically, we generate the discretized image residual errors by identifying the blur pixels and then transform them to a continuous form, which is computationally more efficient than naively solving the original regression problem with continuous values. Here, we found that the discretization result, i.e., blur segmentation map, remarkably exhibits visual similarity with the image residual errors. As a result, our efficient model shows comparable performance to state-of-the-art methods in realistic benchmarks, while our method is up to 10 times computationally more efficient.

翻译：随着移动相机技术的进步使得捕捉高分辨率图像（如4K图像）成为可能，对处理大幅运动的高效去模糊模型的需求日益增加。本文发现，图像残差误差（即模糊-清晰像素差异）可根据运动模糊类型及其邻域像素的复杂程度分为若干类别。受此启发，我们将去模糊（回归）任务分解为模糊像素离散化（像素级模糊分类）和离散到连续转换（基于模糊类别的回归）两个子任务。具体而言，我们通过识别模糊像素生成离散化的图像残差误差，再将其转换为连续形式，这比直接解决原始连续值回归问题在计算上更为高效。值得注意的是，离散化结果（即模糊分割图）在视觉上与图像残差误差高度相似。实验表明，我们的高效模型在真实基准测试中取得了与最先进方法相当的性能，同时计算效率提升高达10倍。