-
Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel
Video deblurring is a challenging problem as the blur is complex and usu...
read it
-
Deep Generative Filter for Motion Deblurring
Removing blur caused by camera shake in images has always been a challen...
read it
-
Rain Streak Removal for Single Image via Kernel Guided CNN
Rain streak removal is an important issue and has recently been investig...
read it
-
Self-Adaptively Learning to Demoire from Focused and Defocused Image Pairs
Moire artifacts are common in digital photography, resulting from the in...
read it
-
Bi-Skip: A Motion Deblurring Network Using Self-paced Learning
A fast and effective motion deblurring method has great application valu...
read it
-
Self-supervised Exposure Trajectory Recovery for Dynamic Blur Estimation
Dynamic scene blurring is an important yet challenging topic. Recently, ...
read it
-
Learning to Synthesize Motion Blur
We present a technique for synthesizing a motion blurred image from a pa...
read it
From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur
Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature of the problem. The predominant solution is to estimate the blur kernel by adding a prior, but the extensive literature on the subject indicates the difficulty in identifying a prior which is suitably informative, and general. Rather than imposing a prior based on theory, we propose instead to learn one from the data. Learning a prior over the latent image would require modeling all possible image content. The critical observation underpinning our approach is thus that learning the motion flow instead allows the model to focus on the cause of the blur, irrespective of the image content. This is a much easier learning task, but it also avoids the iterative process through which latent image priors are typically applied. Our approach directly estimates the motion flow from the blurred image through a fully-convolutional deep neural network (FCN) and recovers the unblurred image from the estimated motion flow. Our FCN is the first universal end-to-end mapping from the blurred image to the dense motion flow. To train the FCN, we simulate motion flows to generate synthetic blurred-image-motion-flow pairs thus avoiding the need for human labeling. Extensive experiments on challenging realistic blurred images demonstrate that the proposed method outperforms the state-of-the-art.
READ FULL TEXT
Comments
There are no comments yet.