Blind image deblurring is a long-standing ill-posed inverse problem which aims to recover a latent sharp image given only a blurry observation. So far, existing studies have designed many effective priors w.r.t. the latent image within the maximum a posteriori (MAP) framework in order to narrow down the solution space. These non-convex priors are always integrated into the final deblurring model, which makes the optimization challenging. However, due to unknown image distribution, complex kernel structure and non-uniform noises in real-world scenarios, it is indeed challenging to explicitly design a fixed prior for all cases. Thus we adopt the idea of adaptive optimization and propose the sparse structure control (SSC) for the latent image during the optimization process. In this paper, we only formulate the necessary optimization constraints in a lightweight MAP model with no priors. Then we develop an inexact projected gradient scheme to incorporate flexible SSC in MAP inference. Besides lp-norm based SSC in our previous work, we also train a group of denoising convolutional neural networks (CNNs) to learn the sparse image structure automatically from the training data under different noise levels, and we show that CNNs-based SSC can achieve similar results compared with lp-norm but are more robust to noise. Extensive experiments demonstrate that the proposed adaptive optimization scheme with two types of SSC achieves the state-of-the-art results on both synthetic data and real-world images.

Key words: blind image deblurring; convolutional neural network (CNN); non-convex optimization; sparse structure control (SSC);