Title: Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training Show Chinese title

Title: 多时间循环神经网络用于渐进式非均匀单图像去模糊的增量时间训练

Abstract: Multi-scale (MS) approaches have been widely investigated for blind single image / video deblurring that sequentially recovers deblurred images in low spatial scale first and then in high spatial scale later with the output of lower scales. MS approaches have been effective especially for severe blurs induced by large motions in high spatial scale since those can be seen as small blurs in low spatial scale. In this work, we investigate alternative approach to MS, called multi-temporal (MT) approach, for non-uniform single image deblurring. We propose incremental temporal training with constructed MT level dataset from time-resolved dataset, develop novel MT-RNNs with recurrent feature maps, and investigate progressive single image deblurring over iterations. Our proposed MT methods outperform state-of-the-art MS methods on the GoPro dataset in PSNR with the smallest number of parameters. Show Chinese abstract

Abstract: 多尺度（MS）方法已被广泛研究用于盲单图像/视频去模糊，这些方法依次首先在低空间尺度上恢复去模糊图像，然后在高空间尺度上恢复，低尺度的输出作为后续处理的基础。 MS 方法在处理由高空间尺度中大运动引起的严重模糊时特别有效，因为这些模糊在低空间尺度上可视为小模糊。 在本工作中，我们研究了一种 MS 的替代方法，称为多时间尺度（MT）方法，用于非均匀单图像去模糊。 我们提出了基于从时间分辨数据集中构建的 MT 层级数据集的增量时间训练，开发了具有循环特征图的新 MT-RNNs，并研究了迭代过程中的渐进单图像去模糊。 我们的 MT 方法在 GoPro 数据集上的 PSNR 指标上优于最先进的 MS 方法，且参数数量最少。