Deblurring using Analysis-Synthesis Networks Pair
Blind image deblurring remains a challenging problem for modern artificial neural networks. Unlike other image restoration problems, deblurring networks fail behind the performance of existing deblurring algorithms in case of uniform and 3D blur models. This follows from the diverse and profound effect that the unknown blur-kernel has on the deblurring operator. We propose a new architecture which breaks the deblurring network into an analysis network which estimates the blur, and a synthesis network that uses this kernel to deblur the image. Unlike existing deblurring networks, this design allows us to explicitly incorporate the blur-kernel in the network's training. In addition, we introduce new cross-correlation layers that allow better blur estimations, as well as unique components that allow the estimate blur to control the action of the synthesis deblurring action. Evaluating the new approach over established benchmark datasets shows its ability to achieve state-of-the-art deblurring accuracy on various tests, as well as offer a major speedup in runtime.
Code (0)
등록된 구현이 없습니다.
Tasks
Blind Image DeblurringDeblurringImage DeblurringImage RestorationSimilar Papers 제목 키워드 기반
Realistic Blur Synthesis for Learning Image Deblurring
Training learning-based deblurring methods demands a tremendous amount of blurred and sharp image pairs. Unfortunately, existing synthetic datasets are not realistic enough, and deblurring models trained on them cannot h…
DeblurringDiversityImage DeblurringRethinking Blur Synthesis for Deep Real-World Image Deblurring
In this paper, we examine the problem of real-world image deblurring and take into account two key factors for improving the performance of the deep image deblurring model, namely, training data synthesis and network arc…
DeblurringImage DeblurringLODE: Deep Local Deblurring and A New Benchmark
While recent deep deblurring algorithms have achieved remarkable progress, most existing methods focus on the global deblurring problem, where the image blur mostly arises from severe camera shake. We argue that the loca…
DeblurringTowards Real-World Video Deblurring by Exploring Blur Formation Process
This paper aims at exploring how to synthesize close-to-real blurs that existing video deblurring models trained on them can generalize well to real-world blurry videos. In recent years, deep learning-based approaches ha…
DeblurringVideo DeblurringLEDNet: Joint Low-light Enhancement and Deblurring in the Dark
Night photography typically suffers from both low light and blurring issues due to the dim environment and the common use of long exposure. While existing light enhancement and deblurring methods could deal with each pro…
DeblurringLow-light Image Deblurring and EnhancementLow-Light Image Enhancement