Towards an Automated Image De-fencing Algorithm Using Sparsity
This addresses image de-fencing for dynamic scenes, but it is incremental as it builds on existing methods for static scenes.
The paper tackles the problem of removing fences from images of dynamic scenes, proposing an automated algorithm that divides the task into fence detection, motion estimation, and data fusion, achieving results through an optimization framework with total variation regularization.
Conventional approaches to image de-fencing suffer from non-robust fence detection and are limited to processing images of static scenes. In this position paper, we propose an automatic de-fencing algorithm for images of dynamic scenes. We divide the problem of image de-fencing into the tasks of automated fence detection, motion estimation and fusion of data from multiple frames of a captured video of the dynamic scene. Fences are detected automatically using two approaches, namely, employing Gabor filter and a machine learning method. We cast the fence removal problem in an optimization framework, by modeling the formation of the degraded observations. The inverse problem is solved using split Bregman technique assuming total variation of the de-fenced image as the regularization constraint.