Academy of Mathematics and Systems Science, CAS
Colloquia & Seminars
| | Speaker: | 金其余 教授，内蒙古大学
| | Inviter: | 陈冲 副研究员 | Title:
| Residual Learning for Effective Demosaicing and Denoising | Time & Venue:
| 2021.11.29 14:30-16:00 腾讯会议ID：689 366 308 | Abstract:
| 会议链接：https://meeting.tencent.com/dm/YKl8dXccOS1K
Image demosaicking and denoising are the two key steps for color image production pipeline. The classical processing sequence consists of applying denoising first, and then demosaicking. However, this sequence leads to oversmoothing and unpleasant checkerboard effect. Moreover, it is very difficult to change this order, because once the image is demosaicked, the statistical properties of the noise will be changed dramatically. This is extremely challenging for traditional denoising models that strongly rely on statistical assumptions. In this paper, we attempt to tackle this prickly problem. Indeed, here we invert the traditional CFA processing pipeline by first demosaicking and then denoising. In the first stage, we design a demosaicking algorithm that combines traditional methods and a convolutional neural network (CNN) to reconstruct a full color image ignoring the noise. To improve the performance in image demosaicking, we modify an Inception architecture for fusing the three R, G and B channels information. This stage retains all known information that is the key point to obtain pleasing final results. After demosaicking, we get a noisy full-color image and use another CNN to learn the demosaicking residual noise (including artifacts) of it, which allows to obtain a restored full color image. Our proposed algorithm completely avoids the checkerboard effect and retains more image detail. Experimental results show that our method clearly outperforms state-of-the-art methods both quantitatively as well as in terms of visual quality.
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