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    Summary
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    This study introduces a new framework for High Dynamic Range (HDR) reconstruction, integrating artifact detection and model optimization. It presents the first HDR artifact dataset (HADataset) and a detector (HADetector) to improve image quality.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Artifacts are a persistent challenge in High Dynamic Range (HDR) reconstruction.
    • Current methods primarily focus on artifact mitigation within model designs, neglecting explicit detection and suppression.
    • The lack of clear artifact boundaries, consistent shapes, and a dedicated dataset hinders direct artifact detection and recovery.

    Purpose of the Study:

    • To propose a unified framework for HDR reconstruction that integrates artifact detection and model optimization.
    • To address the limitations in current HDR reconstruction techniques by introducing explicit artifact handling.
    • To enhance the accuracy and reliability of HDR image quality assessment.

    Main Methods:

    • Construction of the first HDR artifact dataset (HADataset) with 1,213 multi-exposure Low Dynamic Range (LDR) image sets and 1,765 HDR image pairs, including per-pixel artifact annotations.
    • Development of an HDR artifact detector (HADetector) for accurate localization of reconstruction artifacts.
    • Integration of HADetector for fine-tuning existing HDR reconstruction models and as a non-reference image quality assessment (NR-IQA) metric (Artifact Score - AS).

    Main Results:

    • The proposed HADataset provides comprehensive annotations for HDR artifacts.
    • HADetector demonstrates robust performance in detecting and localizing HDR reconstruction artifacts.
    • The fine-tuning approach using HADetector significantly enhances existing HDR reconstruction models.
    • The Artifact Score (AS) metric shows strong alignment with human visual perception for quality evaluation.

    Conclusions:

    • The developed unified framework effectively integrates artifact detection and model optimization for HDR reconstruction.
    • The HADataset and HADetector represent significant contributions to the field, enabling better artifact management.
    • The proposed methods offer a reliable approach for both improving HDR image quality and assessing it accurately.