用窄视角图提升广角图细节，让照片既全又清晰。
Enhancing Wide-Angle Image Using Narrow-Angle View of the Same Scene
- 用GAN从窄视角图提取细节特征，注入广角图
- 通过残差连接与注意力融合，实现高质量细节迁移
- 适合需要兼顾画面范围与细节的摄影与图像增强场景
拍摄场景时常面临两难：广角镜头覆盖更广但细节不足，窄角镜头细节丰富却遗漏部分画面。本文提出一种新方法，利用同一场景的窄视角与广角视角图像，通过基于生成对抗网络（GAN）的模型，学习窄视角图像中的视觉质量特征，并借助残差连接与注意力融合模块将其迁移到对应的广角图像中。该方法可有效分离图像的视觉核心内容并实现跨视角转移。我们在多个基准数据集上进行了实验评估，并与当前先进方法进行了对比，验证了其有效性。
原文摘要 · Abstract (English)
A common dilemma while photographing a scene is whether to capture it at a wider angle, allowing more of the scene to be covered but in less detail or to click in a narrow angle that captures better details but leaves out portions of the scene. We propose a novel method in this paper that infuses wider shots with finer quality details that is usually associated with an image captured by the primary lens by capturing the same scene using both narrow and wide field of view (FoV) lenses. We do so by training a Generative Adversarial Network (GAN)-based model to learn to extract the visual quality parameters from a narrow-angle shot and to transfer these to the corresponding wide-angle image of the scene using residual connections and an attention-based fusion module. We have mentioned in details the proposed technique to isolate the visual essence of an image and to transfer it into another image. We have also elaborately discussed our implementation details and have presented the results of evaluation over several benchmark datasets and comparisons with contemporary advancements in the field.
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