Learning variational models for blind image deconvolution

Along with noise, image blur is probably the most widespread reason for image degradation. It originates from a vast variety of sources, including atmospheric turbulences, defocus and motion. Nowadays fast and accurate deblurring algorithms become more and more important due to the ubiquitous smartphones. The majority of recent deblurring algorithms first estimate the point spread function, also known as blur kernel, and then perform a non-blind image deblurring. In this work we introduce a novel approach for both non-blind and blind image deblurring, which is motivated by variational models.
We follow the idea of Chen et al. 2015 and derive a network structure which is related to minimizing an iteratively adapted energy functional. Moreover, we resent a differentiable projection onto the unit simplex based on the Bregman divergence to constrain the blur kernels. The non-blind as well as blind deblurring networks are trained in a discriminative fashion to enhance properties of natural sharp images because recent discriminative reconstruction approaches demonstrated their superiority in terms of quality and runtime. Both deblurring networks are qualitatively evaluated and numerous experiments demonstrate the clear quality boost of the resulting image and blur kernel estimates. Furthermore, in contrast do neural networks, all individual parameters of the proposed networks can be easily interpreted due to the close relation to energy minimization.