Consistent Filtering of Videos and Dense Light-Fields Without Optic-Flow

Abstract
A convenient post-production video processing approach is to apply image filters on a per-frame basis. This allows the flexibility of extending image filters-originally designed for still images-to videos. However, per-image filtering may lead to temporal inconsistencies perceived as unpleasant flickering artifacts, which is also the case for dense light-fields due to angular inconsistencies. In this work, we present a method for consistent filtering of videos and dense light-fields that addresses these problems. Our assumption is that inconsistencies-due to per-image filtering-are represented as noise across the image sequence. We thus perform denoising across the filtered image sequence and combine per-image filtered results with their denoised versions. At this, we use saliency based optimization weights to produce a consistent output while preserving the details simultaneously. To control the degree-of-consistency in the final output, we implemented our approach in an interactive real-time processing framework. Unlike state-of-the-art inconsistency removal techniques, our approach does not rely on optic-flow for enforcing coherence. Comparisons and a qualitative evaluation indicate that our method provides better results over state-of-the-art approaches for certain types of filters and applications.
Description

        
@inproceedings{
10.2312:vmv.20191326
, booktitle = {
Vision, Modeling and Visualization
}, editor = {
Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
}, title = {{
Consistent Filtering of Videos and Dense Light-Fields Without Optic-Flow
}}, author = {
Shekhar, Sumit
and
Semmo, Amir
and
Trapp, Matthias
and
Tursun, Okan
and
Pasewaldt, Sebastian
and
Myszkowski, Karol
and
Döllner, Jürgen
}, year = {
2019
}, publisher = {
The Eurographics Association
}, ISBN = {
978-3-03868-098-7
}, DOI = {
10.2312/vmv.20191326
} }
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