通过探索变压器的深入性质来消除图像模糊
IEEE transactions on neural networks and learning systems
|February 21, 2024
概括
本研究引入了预训练过的变压器,以改善图像消除模糊,同时提高感知质量和定量得分. 利用变压器功能,它为图像恢复提供了一种新的方法,而不会影响性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 生成模型已经有了先进的图像消除模糊,但同时提高感知质量和定量得分仍然具有挑战性.
- 现有的方法在图像修复中努力平衡视觉真实性与客观性能指标.
研究的目的:
- 引入预训练过的图像消除模糊的变压器,旨在提高感知质量和定量得分.
- 为了利用预训练的视觉转换器 (ViT) 的深度特征,以获得更清晰的图像恢复.
主要方法:
- 利用预训练的视觉转换器 (ViT) 的深度特征来捕捉全球拓图像关系并识别模糊引起的变化.
- 在恢复和目标图像之间比较变压器特征 (矢量差异和分布比较) 的基础上开发了两个新的感知损失.
- 将变压器特征进行比较,以测量图像清晰度并指导消除模糊的过程.
主要成果:
- 在消除模糊的图像中表现出更好的感知质量,而不会牺牲像峰值信号噪声比 (PSNR) 这样的定量得分.
- 超越竞争模式 (Uformer,Restormer,NAFNet) 在失焦和移动消除模糊的任务上.
- 展示了变压器特性在高分辨率图像消除模糊的有效性.
结论:
- 预训练过的变压器为图像消除模糊提供了强大的工具,有效地解决了同时感知和定量改进的挑战.
- 根据变压器特征指导的拟议的感知损失,为更清晰的图像恢复提供了可靠的方法.
- 这种方法推进了图像消除模糊的最先进技术,特别是在复杂的场景中,如失焦和运动模糊.
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