Blind Motion Deblurring with Pixel-Wise Kernel Estimation via Kernel Prediction Networks

In recent years, the removal of motion blur in photographs has seen impressive progress in the hands of deep learning-based methods, trained to map directly from blurry to sharp images. For this reason, approaches that explicitly use a forward degradation model received significantly less attention. However, a well-defined specification of the blur genesis, as an intermediate step, promotes the generalization and explainability of the method. Towards this goal, we propose a learning-based motion deblurring method based on dense non-uniform motion blur estimation followed by a non-blind deconvolution approach. Specifically, given a blurry image, a first network estimates the dense per-pixel motion blur kernels using a lightweight representation composed of a set of image-adaptive basis motion kernels and the corresponding mixing coefficients. Then, a second network trained jointly with the first one, unrolls a non-blind deconvolution method using the motion kernel field estimated by the first network. The model-driven aspect is further promoted by training the networks on sharp/blurry pairs synthesized according to a convolution-based, non-uniform motion blur degradation model. Qualitative and quantitative evaluation shows that the kernel prediction network produces accurate motion blur estimates, and that the deblurring pipeline leads to restorations of real blurred images that are competitive or superior to those obtained with existing end-to-end deep learning-based methods. Code and trained models are available at https://github.com/GuillermoCarbajal/J-MKPD/.

Rethinking Motion Deblurring Training: A Segmentation-Based Method for Simulating Non-Uniform Motion Blurred Images

Successful training of end-to-end deep networks for real motion deblurring requires datasets of sharp/blurred image pairs that are realistic and diverse enough to achieve generalization to real blurred images. Obtaining such datasets remains a challenging task. In this paper, we first review the limitations of existing deblurring benchmark datasets from the perspective of generalization to blurry images in the wild. Secondly, we propose an efficient procedural methodology to generate sharp/blurred image pairs, based on a simple yet effective model for the formation of blurred images. This allows generating virtually unlimited realistic and diverse training pairs. We demonstrate the effectiveness of the proposed dataset by training existing deblurring architectures on the simulated pairs and evaluating them across four standard datasets of real blurred images. We observed superior generalization performance for the ultimate task of deblurring real motion-blurred photos of dynamic scenes when training with the proposed method.

Single Image Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition

Characterizing and removing motion blur caused by camera shake or object motion remains an important task for image restoration. In recent years, removal of motion blur in photographs has seen impressive progress in the hands of deep learning-based methods, trained to map directly from blurry to sharp images. Characterization of motion blur, on the other hand, has received less attention and progress in model-based methods for restoration lags behind that of data-driven end-to-end approaches. In this paper, we propose a general, non-parametric model for dense non-uniform motion blur estimation. Given a blurry image, we estimate a set of adaptive basis kernels as well as the mixing coefficients at pixel level, producing a per-pixel map of motion blur. This rich but efficient forward model of the degradation process allows the utilization of existing tools for solving inverse problems. We show that our method overcomes the limitations of existing non-uniform motion blur estimation and that it contributes to bridging the gap between model-based and data-driven approaches for deblurring real photographs.