论文标题
直接手持式爆破成像以模拟散焦
Direct Handheld Burst Imaging to Simulated Defocus
论文作者
论文摘要
浅深度图像使对象保持焦点，前景和背景上下文模糊。这种效果需要比智能手机相机的镜头大得多。常规方法根据其深度获取RGB-D图像和模糊图像区域。但是，这种方法不适用于反射性或透明的表面，也不适用于深度值不准确或模棱两可的对象轮廓。 我们提出了一种基于学习的方法，可以在用单个小光圈镜头获得的手持式爆发中综合降水模糊。我们的深度学习模型直接产生了浅层场图像，避免了明显的基于深度的模糊。模拟的孔径直径等于爆发过程中的相机翻译。由于不准确或模棱两可的深度估计，我们的方法不会遭受伪影的困扰，并且非常适合肖像摄影。
A shallow depth-of-field image keeps the subject in focus, and the foreground and background contexts blurred. This effect requires much larger lens apertures than those of smartphone cameras. Conventional methods acquire RGB-D images and blur image regions based on their depth. However, this approach is not suitable for reflective or transparent surfaces, or finely detailed object silhouettes, where the depth value is inaccurate or ambiguous. We present a learning-based method to synthesize the defocus blur in shallow depth-of-field images from handheld bursts acquired with a single small aperture lens. Our deep learning model directly produces the shallow depth-of-field image, avoiding explicit depth-based blurring. The simulated aperture diameter equals the camera translation during burst acquisition. Our method does not suffer from artifacts due to inaccurate or ambiguous depth estimation, and it is well-suited to portrait photography.
