Abstract

Dynamic scene blur is usually caused by object motion, depth variation as well as camera shake.Most existing methods usually solve this problem using image segmentation or fully end-to-end trainable deep convolutional neural networks by considering different object motions or camera shakes.However, these algorithms are less effective when there exist depth variations. In this work, we propose a deep convolutional network that exploits the depth prior information for dynamic scene deblurring. Given a blurred image, we first extract the depth map and adopt a depth refinement network to restore the edges and structure in the depth map. Instead of using a feature concatenation, we adopt the spatial feature transform layer to extract depth features and effectively fuse with the image features through scaling and shifting. Our image deblurring network thus learns to restore clear images under the guidance of the depth prior. With substantial experiments and analysis, we show that the depth information is crucial to the performance of the proposed model. Finally, extensive quantitative and qualitative evaluations demonstrate that the proposed model performs favorably against the state-of-the-art dynamic scene deblurring approaches as well as conventional depth-based deblurring algorithms..

Technical Papers and Codes

[Paper] [Code ] [Pretrained Model] (coming soon...)

Experimental Results

[Extension to video deblurring]