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首页> 外文会议>IEEE/CVF Conference on Computer Vision and Pattern Recognition >Wavelet Synthesis Net for Disparity Estimation to Synthesize DSLR Calibre Bokeh Effect on Smartphones
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Wavelet Synthesis Net for Disparity Estimation to Synthesize DSLR Calibre Bokeh Effect on Smartphones

机译：小波综合网用于视差估计以合成智能手机的单反口径散景效果

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Modern smartphone cameras can match traditional DSLR cameras in many areas thanks to the introduction of camera arrays and multi-frame processing. Among all types of DSLR effects, the narrow depth of field (DoF) or so called bokeh probably arouses most interest. Today's smartphones try to overcome the physical lens and sensor limitations by introducing computational methods that utilize a depth map to synthesize the narrow DoF effect from all-in-focus images. However, a high quality depth map remains to be the key differentiator between computational bokeh and DSLR optical bokeh. Empowered by a novel wavelet synthesis network architecture, we have greatly narrowed the gap between DSLR and smartphone camera in terms of the bokeh more than ever before. We describe three key Modern smartphone cameras can match traditional digital single lens reflex (DSLR) cameras in many areas thanks to the introduction of camera arrays and multi-frame processing. Among all types of DSLR effects, the narrow depth of field (DoF) or so called bokeh probably arouses most interest. Today's smartphones try to overcome the physical lens and sensor limitations by introducing computational methods that utilize a depth map to synthesize the narrow DoF effect from all-in-focus images. However, a high quality depth map remains to be the key differentiator between computational bokeh and DSLR optical bokeh. Empowered by a novel wavelet synthesis network architecture, we have narrowed the gap between DSLR and smartphone camera in terms of bokeh more than ever before. We describe three key enablers of our bokeh solution: a synthetic graphics engine to generate training data with precisely prescribed characteristics that match the real smartphone captures, a novel wavelet synthesis neural network (WSN) architecture to produce unprecedented high definition disparity map promptly on smartphones, and a new evaluation metric to quantify the quality of the disparity map for real images from the bokeh rendering perspective. Experimental results show that the disparity map produced from our neural network achieves much better accuracy than the other state-of-the-art CNN based algorithms. Combining the high resolution disparity map with our rendering algorithm, we demonstrate visually superior bokeh pictures compared with existing top rated flagship smartphones listed on the DXOMARK mobiles.
机译：由于引入相机阵列和多帧处理，现代智能手机相机可以匹配传统的DSLR摄像机。在所有类型的DSLR效应中，狭窄的景深（DOF）或所谓的散景可能引起最多的兴趣。今天的智能手机尝试通过引入利用深度图来克服来自全焦焦图像的窄DOF效果的计算方法来克服物理镜头和传感器限制。然而，高质量深度图仍然是计算散景和DSLR光学散景的关键区分器。由新型小波综合网络架构赋予，我们在比以往任何时候都大大缩小了DSLR和智能手机相机之间的差距。我们描述了三个关键的现代智能手机相机，可以在许多领域匹配传统的数字单镜头反射（DSLR）相机，因为引入了相机阵列和多帧处理。在所有类型的DSLR效应中，狭窄的景深（DOF）或所谓的散景可能引起最多的兴趣。今天的智能手机尝试通过引入利用深度图来克服来自全焦焦图像的窄DOF效果的计算方法来克服物理镜头和传感器限制。然而，高质量深度图仍然是计算散景和DSLR光学散景的关键区分器。由新型小波综合网络架构赋予，我们比以往任何时候都在散景的方面缩小了DSLR和智能手机相机之间的差距。我们描述了我们Bokeh解决方案的三个关键推动因素：一种合成图形引擎，用于生成具有与真正的规定特性的培训数据，与真正的智能手机捕获，一个新的小波合成神经网络（WSN）架构，以便在智能手机上迅速生成前所未有的高清视差地图，和新的评估度量标准量化来自Bokeh渲染透视的真实图像的视差图的质量。实验结果表明，从我们的神经网络生产的差异图达到了比其他最先进的基于CNN的算法更好的准确性。将高分辨率视差图与我们的渲染算法相结合，我们展示了视觉上优越的散景图片与DXMark Mobiles上列出的现有最高额定旗舰智能手机相比。

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