Abstract
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.
Abstract (translated)
随着移动相机技术的最新进展，已经能够捕捉到高分辨率图像，如4K图像，对大运动模糊的有效的模糊模型处理需求增加了。在本文中，我们发现根据模糊类型的不同，图像残差误差可以分为一些类别。为了验证这个想法，我们分解了模糊（回归）任务为模糊像素离散化（像素级模糊分类）和离散-到连续转换（带有模糊类图的回归）任务。具体来说，我们通过识别模糊像素并将其转换为连续形式，使得计算更加高效，而原始回归问题使用连续值求解在计算上更加昂贵。在这里，我们发现离散化结果，即模糊分割图，与图像残差误差具有显著的视觉相似性。因此，我们的有效模型在现实基准测试中的性能与最先进的 methods相当，而我们的方法比传统方法在计算上效率高达10倍。
URL
https://arxiv.org/abs/2404.12168
