Automatic Flare Spot Artifact Detection and Removal in Photographs
- URL: http://arxiv.org/abs/2103.04384v1
- Date: Sun, 7 Mar 2021 15:51:49 GMT
- Title: Automatic Flare Spot Artifact Detection and Removal in Photographs
- Authors: Patricia Vitoria and Coloma Ballester
- Abstract summary: Flare spot is one type of flare artifact caused by a number of conditions.
In this paper, we propose a robust computational method to automatically detect and remove flare spot artifacts.
- Score: 4.56877715768796
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Flare spot is one type of flare artifact caused by a number of conditions,
frequently provoked by one or more high-luminance sources within or close to
the camera field of view. When light rays coming from a high-luminance source
reach the front element of a camera, it can produce intra-reflections within
camera elements that emerge at the film plane forming non-image information or
flare on the captured image. Even though preventive mechanisms are used,
artifacts can appear. In this paper, we propose a robust computational method
to automatically detect and remove flare spot artifacts. Our contribution is
threefold: firstly, we propose a characterization which is based on intrinsic
properties that a flare spot is likely to satisfy; secondly, we define a new
confidence measure able to select flare spots among the candidates; and,
finally, a method to accurately determine the flare region is given. Then, the
detected artifacts are removed by using exemplar-based inpainting. We show that
our algorithm achieve top-tier quantitative and qualitative performance.
Related papers
- Toward Flare-Free Images: A Survey [1.0878040851637998]
Lens flare is an artifact that can significantly degrade image quality and affect the performance of computer vision systems.
This survey delves into the complex optics of flare formation, arising from factors like internal reflection, scattering, diffraction, and dispersion within the camera lens system.
The survey extensively covers the wide range of methods proposed for flare removal, including hardware optimization strategies, classical image processing techniques, and learning-based methods using deep learning.
arXiv Detail & Related papers (2023-10-22T16:46:12Z) - Improving Lens Flare Removal with General Purpose Pipeline and Multiple
Light Sources Recovery [69.71080926778413]
flare artifacts can affect image visual quality and downstream computer vision tasks.
Current methods do not consider automatic exposure and tone mapping in image signal processing pipeline.
We propose a solution to improve the performance of lens flare removal by revisiting the ISP and design a more reliable light sources recovery strategy.
arXiv Detail & Related papers (2023-08-31T04:58:17Z) - AltFreezing for More General Video Face Forgery Detection [138.5732617371004]
We propose to capture both spatial and unseen temporal artifacts in one model for face forgery detection.
We present a novel training strategy called AltFreezing for more general face forgery detection.
arXiv Detail & Related papers (2023-07-17T08:24:58Z) - Flare7K++: Mixing Synthetic and Real Datasets for Nighttime Flare
Removal and Beyond [77.72043833102191]
We introduce the first comprehensive nighttime flare removal dataset, consisting of 962 real-captured flare images (Flare-R) and 7,000 synthetic flares (Flare7K)
Compared to Flare7K, Flare7K++ is particularly effective in eliminating complicated degradation around the light source, which is intractable by using synthetic flares alone.
To address this issue, we additionally provide the annotations of light sources in Flare7K++ and propose a new end-to-end pipeline to preserve the light source while removing lens flares.
arXiv Detail & Related papers (2023-06-07T08:27:44Z) - Nighttime Smartphone Reflective Flare Removal Using Optical Center
Symmetry Prior [81.64647648269889]
Reflective flare is a phenomenon that occurs when light reflects inside lenses, causing bright spots or a "ghosting effect" in photos.
We propose an optical center symmetry prior, which suggests that the reflective flare and light source are always symmetrical around the lens's optical center.
We create the first reflective flare removal dataset called BracketFlare, which contains diverse and realistic reflective flare patterns.
arXiv Detail & Related papers (2023-03-27T09:44:40Z) - Flare7K: A Phenomenological Nighttime Flare Removal Dataset [83.38205781536578]
We introduce Flare7K, the first nighttime flare removal dataset.
It offers 5,000 scattering and 2,000 reflective flare images, consisting of 25 types of scattering flares and 10 types of reflective flares.
With the paired data, we can train deep models to restore flare-corrupted images taken in the real world effectively.
arXiv Detail & Related papers (2022-10-12T20:17:24Z) - Robust Glare Detection: Review, Analysis, and Dataset Release [6.281101654856357]
Sun Glare widely exists in the images captured by unmanned ground and aerial vehicles performing in outdoor environments.
The source of glare is not limited to the sun, and glare can be seen in the images captured during the nighttime and in indoor environments.
This research aims to introduce the first dataset for glare detection, which includes images captured by different cameras.
arXiv Detail & Related papers (2021-10-12T13:46:33Z) - Identifying Invariant Texture Violation for Robust Deepfake Detection [17.306386179823576]
We propose the Invariant Texture Learning framework, which only accesses the published dataset with low visual quality.
Our method is based on the prior that the microscopic facial texture of the source face is inevitably violated by the texture transferred from the target person.
arXiv Detail & Related papers (2020-12-19T03:02:15Z) - How to Train Neural Networks for Flare Removal [45.51943926089249]
We train neural networks to remove lens flare for the first time.
Our data synthesis approach is critical for accurate flare removal.
Models trained with our technique generalize well to real lens flares across different scenes, lighting conditions, and cameras.
arXiv Detail & Related papers (2020-11-25T02:23:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.