Single image blind deblurring is highly ill-posed as neither the latent sharp image nor the blur kernel is known. Even though considerable progress has been made, several major difficulties remain for blind deblurring, including the trade-off between high-performance deblurring and real-time processing. Besides, we observe that current single image blind deblurring networks cannot further improve or stabilize the performance but significantly degrades the performance when re-deblurring is repeatedly applied. This implies the limitation of these networks in modeling an ideal deblurring process. In this work, we make two contributions to tackle the above difficulties: (1) We introduce the idempotent constraint into the deblurring framework and present a deep idempotent network to achieve improved blind non-uniform deblurring performance with stable re-deblurring. (2) We propose a simple yet efficient deblurring network with lightweight encoder-decoder units and a recurrent structure that can deblur images in a progressive residual fashion. Extensive experiments on synthetic and realistic datasets prove the superiority of our proposed framework. Remarkably, our proposed network is nearly 6.5X smaller and 6.4X faster than the state-of-the-art while achieving comparable high performance.
翻译:单个图像失明的破坏是极为糟糕的,因为潜伏的锐利图像和模糊的内核都不得而知。尽管已经取得了相当大的进展,但盲人的破坏仍然存在若干重大困难,包括高性能的破坏和实时处理之间的权衡。此外,我们注意到,目前单个图像失明的破坏网络无法进一步改善或稳定性能,而是在反复适用再破坏时显著降低性能。这意味着这些网络在模拟理想的破坏过程方面受到限制。在这项工作中,我们为解决上述困难作出了两项贡献:(1) 我们把极坏的限制因素引入了分解框架,并展示了一种深厚的罪恶网络,以稳定地再破坏性能来改进失明的不统一破坏性工作。(2) 我们提议建立一个简单而有效的破坏性能网络,其重量轻的分解码装置和经常性结构可以逐渐地破坏理想的破坏性能。在合成和现实性数据配置方面的广泛实验证明了我们提议的框架的优越性,而我们提议的高性能网络则比6.5和高。