  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.
