Non-uniform blind motion deblurring is a challenging yet important problem in image processing that receives enduring attention in the last decade. The non-uniformity nature of motion blurring leads to great variations on the blurring effects across image regions and over different images, which makes it very difficult to train an end-to-end deblurring neural network (NN) with good generalization performance. This paper introduces an attention mechanism for the blind deblurring NN, including both spatial and channel attention, so as to effectively handle the significant spatial variations on blurring effects. In the attention mechanism, the spatial attention is introduced in both the encoder for discriminative exploitation of image edges and smooth regions and the decoder for discriminative treatment on different regions with different blurring effects. The channel attention is introduced for better generalization performance of the NN, as it allows adaptive weighting on intermediate features for a particular image. Building such an attention mechanism into a multi-scale encoder–decoder framework, an attentive NN is developed for practical non-uniform blind image deblurring. The experiments on several benchmark datasets show that the proposed NN can effectively restore the images degraded by spatially-varying blurring, with state-of-the-art performance.

在过去的十年中，不均匀的盲运动去模糊是图像处理中一个具有挑战性但重要的问题，受到了持续的关注。运动模糊的非均匀性导致跨图像区域和跨不同图像的模糊效果发生巨大变化，这使得训练具有良好泛化性能的端到端去模糊神经网络（NN）变得非常困难。本文介绍了一种用于去模糊NN的注意机制，包括空间注意和通道注意，以有效处理模糊效果上的显着空间变化。在注意力机制上 在对图像边缘和平滑区域进行判别性利用的编码器以及对具有不同模糊效果的不同区域进行判别性处理的解码器中都引入了空间注意。引入通道注意是为了更好地实现NN的泛化性能，因为它允许对特定图像的中间特征进行自适应加权。将这种关注机制构建到多尺度编码器-解码器框架中，可以开发出一种专心的NN，用于实际的非均匀盲图像去模糊。在几个基准数据集上进行的实验表明，所提出的神经网络可以有效地恢复因空间变化的模糊而退化的图像，并具有最新的性能。因为它允许对特定图像的中间特征进行自适应加权。将这种关注机制构建到多尺度编码器-解码器框架中，可以开发出一种专心的NN，用于实际的非均匀盲图像去模糊。在几个基准数据集上进行的实验表明，所提出的神经网络可以有效地恢复因空间变化的模糊而退化的图像，并具有最新的性能。因为它允许对特定图像的中间特征进行自适应加权。将这种关注机制构建到多尺度编码器-解码器框架中，可以开发出一种专心的NN，用于实际的非均匀盲图像去模糊。在几个基准数据集上进行的实验表明，所提出的神经网络可以有效地恢复因空间变化的模糊而退化的图像，并具有最新的性能。