Computational imaging
Computational imaging
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DOI：
10.1109/isscc.2012.6177116
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发表时间：
2012-02
期刊：
影响因子：
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通讯作者：
M. Ikeda;Cynthia Yin;Johannes Solhusvik;J. Bosiers
中科院分区：
文献类型：
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作者：
M. Ikeda;Cynthia Yin;Johannes Solhusvik;J. Bosiers
计算成像正被广泛应用于消费产品中，从特殊光学和图像传感器提供的原始像素数据重建高质量的图像。本论坛将提供这些系统的细节。我们将开始与计算摄影和成像的概述。接下来是对象识别和跟踪技术，包括有趣的点技术和针对相机优化的人脸检测算法。将介绍用于提高分辨率和扩展景深的相机阵列技术、多镜头技术和编码光圈技术。本文还介绍了目前流行的压缩感知技术，目的是在不严重影响图像质量的情况下降低数据速率。最后，介绍了并行处理体系结构的现有实现。
Computational imaging is becoming widely adopted in consumer products to reconstruct high quality pictures from raw pixel data provided by special optics and image sensors. This forum will provide details of such systems. We will commence with an overview of computational photograpy and imaging. This is followed by object recognition and tracking techniques including interesting point techniques and face detection algorithms optimized for cameras. Camera array techniques, multiple shot techniques and coded aperture techniques for improved resolution and extended depth of field will be presented. The popular compressed sensing technique is also covered with the aim to reduce data rate without severely impacting image quality. Lastly, existing implementation on parallel processing architecture will be introduced.