Image deblurring is a challenging problem in imaging due to its highly ill-posed nature. Deep learning models have shown great success in tackling this problem but the quest for the best image quality has brought their computational complexity up, making them impractical on anything but powerful servers. Meanwhile, recent works have shown that mobile Lidars can provide complementary information in the form of depth maps that enhance deblurring quality. In this paper, we introduce a novel low-complexity neural network for depth-guided image deblurring. We show that the use of the wavelet transform to separate structural details and reduce spatial redundancy as well as efficient feature conditioning on the depth information are essential ingredients in developing a low-complexity model. Experimental results show competitive image quality against recent state-of-the-art models while reducing complexity by up to two orders of magnitude.

翻译：图像去模糊因其高度不适定性而成为成像领域中的一个具有挑战性的问题。深度学习模型在解决此问题上已显示出巨大成功，但对最佳图像质量的追求使其计算复杂度不断攀升，导致它们只能在强大的服务器上运行，缺乏实用性。与此同时，近期研究表明，移动激光雷达能够以深度图的形式提供补充信息，从而提升去模糊质量。本文提出了一种新颖的低复杂度神经网络，用于深度引导的图像去模糊。我们证明了利用小波变换来分离结构细节并减少空间冗余，以及对深度信息进行高效特征条件化，是开发低复杂度模型的关键要素。实验结果表明，与当前最先进的模型相比，该方法在图像质量上具有竞争力，同时将复杂度降低了多达两个数量级。