Kalman Filtering of Patches for Frame-Recursive Video Denoising

Pablo Arias, Jean-Michel Morel; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


A frame recursive video denoising method computes each output frame as a function of only the current noisy frame and the previous denoised output. Frame recursive methods were among the earliest approaches for video denoising. However in the last fifteen years they have been used almost exclusively for real-time applications with denoising performance far from being state-of-the-art. In this work we propose a simple frame recursive method which is fast, has a low memory complexity and achieves results competitive with more complex state-of-the-art methods that require processing several input frames for producing each output frame. Furthermore, in terms of visual quality, the proposed approach is able to recover many details that are missed by most non-recursive methods. As an additional contribution we also propose an off-line post-processing of the denoised video that boosts denoising quality and temporal consistency.

Related Material


[pdf]
[bibtex]
@InProceedings{Arias_2019_CVPR_Workshops,
author = {Arias, Pablo and Morel, Jean-Michel},
title = {Kalman Filtering of Patches for Frame-Recursive Video Denoising},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}