Robust Kernel Estimation With Outliers Handling for Image Deblurring
Estimating blur kernels from real world images is a challenging problem as the linear image formation assumption does not hold when significant outliers, such as saturated pixels and non-Gaussian noise, are present. While some existing non-blind deblurring algorithms can deal with outliers to a certain extent, few blind deblurring methods are developed to well estimate the blur kernels from the blurred images with outliers. In this paper, we present an algorithm to address this problem by exploiting reliable edges and removing outliers in the intermediate latent images, thereby estimating blur kernels robustly. We analyze the effects of outliers on kernel estimation and show that most state-of-the-art blind deblurring methods may recover delta kernels when blurred images contain significant outliers. We propose a robust energy function which describes the properties of outliers for the final latent image restoration. Furthermore, we show that the proposed algorithm can be applied to improve existing methods to deblur images with outliers. Extensive experiments on different kinds of challenging blurry images with significant amount of outliers demonstrate the proposed algorithm performs favorably against the state-of-the-art methods.
Code (0)
등록된 구현이 없습니다.
Tasks
DeblurringImage DeblurringImage RestorationSimilar Papers 제목 키워드 기반
Blind Image Deblurring With Outlier Handling
Deblurring images with outliers has attracted considerable attention recently. However, existing algorithms usually involve complex operations which increase the difficulty of blur kernel estimation. In this paper, we pr…
Blind Image DeblurringDeblurringImage DeblurringOutlier DetectionSelf-Paced Kernel Estimation for Robust Blind Image Deblurring
The challenge in blind image deblurring is to remove the effects of blur with limited prior information about the nature of the blur process. Existing methods often assume that the blur image is produced by linear convol…
Blind Image DeblurringDeblurringImage DeblurringOID: Outlier Identifying and Discarding in Blind Image Deblurring
Blind deblurring methods are sensitive to outliers, such as saturated pixels and non-Gaussian noise. Even a small amount of outliers can dramatically degrade the quality of the estimated blur kernel, because the outliers…
Blind Image DeblurringDeblurringImage DeblurringSelf-Supervised Multi-Scale Network for Blind Image Deblurring via Alternating Optimization
Blind image deblurring is a challenging low-level vision task that involves estimating the unblurred image when the blur kernel is unknown. In this paper, we present a self-supervised multi-scale blind image deblurring m…
Blind Image DeblurringDeblurringImage DeblurringUnderstanding Kernel Size in Blind Deconvolution
Most blind deconvolution methods usually pre-define a large kernel size to guarantee the support domain. Blur kernel estimation error is likely to be introduced, yielding severe artifacts in deblurring results. In this p…
Deblurring