{
    "abstract": "Lens flare is a common phenomenon when strong light rays arrive at the camera sensor and a clean scene is consequently mixed up with various opaque and semi-transparent artifacts. Existing deep learning methods are always constrained with limited real image pairs for training. Though recent synthesis-based approaches are found effective, synthesized pairs still deviate from the real ones as the mixing mechanism of flare artifacts and scenes in the wild always depends on a line of undetermined factors, such as lens structure, scratches, etc. In this paper, we present a new perspective from the blind nature of the flare removal task in a knowledge-driven manner. Specifically, we present a simple yet effective flare-level estimator to predict the corruption level of a flare-corrupted image. The estimated flare-level can be interpreted as additive information of the gap between corrupted images and their flare-free correspondences to facilitate a network at both training and testing stages adaptively. Besides, we utilize a flare-level modulator to better integrate the estimations into networks. We also devise a flare-aware block for more accurate flare recognition and reconstruction. Additionally, we collect a new real-world flare dataset for benchmarking, namely WiderFlare. Extensive experiments on three benchmark datasets demonstrate that our method outperforms state-of-the-art methods quantitatively and qualitatively.",
    "authors": [
        {
            "affiliation": null,
            "firstname": "Haoyou",
            "initials": "H",
            "lastname": "Deng"
        },
        {
            "affiliation": null,
            "firstname": "Lida",
            "initials": "L",
            "lastname": "Li"
        },
        {
            "affiliation": null,
            "firstname": "Feng",
            "initials": "F",
            "lastname": "Zhang"
        },
        {
            "affiliation": null,
            "firstname": "Zhiqiang",
            "initials": "Z",
            "lastname": "Li"
        },
        {
            "affiliation": null,
            "firstname": "Bin",
            "initials": "B",
            "lastname": "Xu"
        },
        {
            "affiliation": null,
            "firstname": "Qingbo",
            "initials": "Q",
            "lastname": "Lu"
        },
        {
            "affiliation": null,
            "firstname": "Changxin",
            "initials": "C",
            "lastname": "Gao"
        },
        {
            "affiliation": null,
            "firstname": "Nong",
            "initials": "N",
            "lastname": "Sang"
        }
    ],
    "conclusions": null,
    "copyrights": null,
    "doi": "10.1109/TIP.2024.3480696",
    "journal": "IEEE transactions on image processing : a publication of the IEEE Signal Processing Society",
    "keywords": [],
    "methods": null,
    "publication_date": "2024-10-22",
    "pubmed_id": "39437280",
    "results": null,
    "title": "Toward Blind Flare Removal Using Knowledge-Driven Flare-Level Estimator.",
    "xml": "<Element 'PubmedArticle' at 0x788506bcaac0>"
}