SVBR-NET: A Non-Blind Spatially Varying Defocus Blur Removal Network
This addresses image degradation for photography and computer vision applications, but it is incremental as it builds on existing blur estimation methods.
The paper tackles the problem of removing spatially varying defocus blur from images by proposing a non-blind deblurring network that uses two encoder-decoder sub-networks with skip connections, and it demonstrates effectiveness when combined with various blur estimation methods in experiments.
Defocus blur is a physical consequence of the optical sensors used in most cameras. Although it can be used as a photographic style, it is commonly viewed as an image degradation modeled as the convolution of a sharp image with a spatially-varying blur kernel. Motivated by the advance of blur estimation methods in the past years, we propose a non-blind approach for image deblurring that can deal with spatially-varying kernels. We introduce two encoder-decoder sub-networks that are fed with the blurry image and the estimated blur map, respectively, and produce as output the deblurred (deconvolved) image. Each sub-network presents several skip connections that allow data propagation from layers spread apart, and also inter-subnetwork skip connections that ease the communication between the modules. The network is trained with synthetically blur kernels that are augmented to emulate blur maps produced by existing blur estimation methods, and our experimental results show that our method works well when combined with a variety of blur estimation methods.