Light Source Guided Single-Image Flare Removal from Unpaired Data

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

12 Scopus Citations
View graph of relations

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision
Subtitle of host publicationICCV 2021
PublisherIEEE
Pages4157-4165
ISBN (Electronic)9781665428125
ISBN (Print)978-1-6654-2813-2
Publication statusPublished - Oct 2021

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499
ISSN (Electronic)2380-7504

Conference

Title18th IEEE/CVF International Conference on Computer Vision (ICCV 2021)
LocationVirtual
PlaceCanada
CityMontreal, QC
Period11 - 17 October 2021

Abstract

Causally-taken images often suffer from flare artifacts, due to the unintended reflections and scattering of light inside the camera. However, as flares may appear in a variety of shapes, positions, and colors, detecting and removing them entirely from an image is very challenging. Existing methods rely on predefined intensity and geometry priors of flares, and may fail to distinguish the difference between light sources and flare artifacts. We observe that the conditions of the light source in the image play an important role in the resulting flares. In this paper, we present a deep framework with light source aware guidance for single-image flare removal (SIFR). In particular, we first detect the light source regions and the flare regions separately, and then remove the flare artifacts based on the light source aware guidance. By learning the underlying relationships between the two types of regions, our approach can remove different kinds of flares from the image. In addition, instead of using paired training data which are difficult to collect, we propose the first unpaired flare removal dataset and new cycle-consistency constraints to obtain more diverse examples and avoid manual annotations. Extensive experiments demonstrate that our method outperforms the baselines qualitatively and quantitatively. We also show that our model can be applied to flare effect manipulation (e.g., adding or changing image flares).

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Light Source Guided Single-Image Flare Removal from Unpaired Data. / Qiao, Xiaotian; Hancke, Gerhard P.; Lau, Rynson W.H.
Proceedings - 2021 IEEE/CVF International Conference on Computer Vision: ICCV 2021. IEEE, 2021. p. 4157-4165 (Proceedings of the IEEE International Conference on Computer Vision).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review