Cascaded image deblurring with combined image prior

In this paper, we propose a cascaded image deblurring scheme that adopts combined image prior followed by multi-scale separable kernel refinement. Specifically, we introduce a combined image prior made up of Aggressive Patch Recurrence (APR) prior and sparsity image prior, where the sparsity prior works as a good compensation for cases where APR prior fails to maintain the self-similarity assumption. We further propose a multi-scale separable kernel refinement method, which can effectively remove noise and ringing artifacts from kernels. Experimental results show that our cascaded deblurring scheme is capable of estimating noise-free blur kernels and achieves visually pleasant resultant images without obvious ringing artifacts when deblurring both synthetic and real-world blurry images.