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首页> 外文期刊>ACM Transactions on Graphics >High-Quality Image Deblurring with Panchromatic Pixels
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High-Quality Image Deblurring with Panchromatic Pixels

机译:具有全色像素的高质量图像去模糊

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摘要

Image deblurring has been a very challenging problem in recent decades. In this article, we propose a high-quality image deblurring method with a novel image prior based on a new imaging system. The imaging system has a newly designed sensor pattern achieved by adding panchromatic (pan) pixels to the conventional Bayer pattern. Since these pan pixels are sensitive to all wavelengths of visible light, they collect a significantly higher proportion of the light striking the sensor. A new demosaicing algorithm is also proposed to restore full-resolution images from pixels on the sensor. The shutter speed of pan pixels is controllable to users. Therefore, we can produce multiple images with different exposures. When long exposure is needed under dim light, we read pan pixels twice in one shot: one with short exposure and the other with long exposure. The long-exposure image is often blurred, while the short-exposure image can be sharp and noisy. The short-exposure image plays an important role in deblurring, since it is sharp and there is no alignment problem for the one-shot image pair. For the algorithmic aspect, our method runs in a two-step maximum-a-posteriori (MAP) fashion under a joint minimization of the blur kernel and the deblurred image. The algorithm exploits a combined image prior with a statistical part and a spatial part, which is powerful in ringing controls. Extensive experiments under various conditions and settings are conducted to demonstrate the performance of our method.
机译:近几十年来,图像去模糊一直是一个非常具有挑战性的问题。在本文中,我们基于新的成像系统提出了一种具有新颖图像先验的高质量图像去模糊方法。该成像系统具有新设计的传感器图案,该传感器图案是通过将全色(pan)像素添加到常规Bayer图案中而实现的。由于这些全像素对可见光的所有波长都敏感,因此它们收集了撞击传感器的光的比例要高得多。还提出了一种新的去马赛克算法,以从传感器上的像素恢复全分辨率图像。用户可以控制全景像素的快门速度。因此,我们可以产生具有不同曝光量的多个图像。当需要在昏暗的光线下长时间曝光时,我们可以一次拍摄两次全像素图像:一次曝光短,而另一次曝光长。长时间曝光的图像通常模糊不清,而短时间曝光的图像可能清晰且嘈杂。短曝光图像在去模糊方面起着重要作用,因为它很清晰并且单次拍摄图像对没有对齐问题。对于算法方面,我们的方法在模糊核和去模糊图像的联合最小化下以两步最大后验(MAP)的方式运行。该算法利用具有统计部分和空间部分的组合图像,这在振铃控制中功能强大。在各种条件和设置下进行了广泛的实验,以证明我们方法的性能。

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