Wu Xiaotian, Li Jiongcheng, Zhou Guanxing, Lü Bo, Li Qingqing, Yang Hang
College of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
Sensors (Basel). 2021 May 11;21(10):3317. doi: 10.3390/s21103317.
The simple lens computational imaging method provides an alternative way to achieve high-quality photography. It simplifies the design of the optical-front-end to a single-convex-lens and delivers the correction of optical aberration to a dedicated computational restoring algorithm. Traditional single-convex-lens image restoration is based on optimization theory, which has some shortcomings in efficiency and efficacy. In this paper, we propose a novel Recursive Residual Groups network under Generative Adversarial Network framework (RRG-GAN) to generate a clear image from the aberrations-degraded blurry image. The RRG-GAN network includes dual attention module, selective kernel network module, and residual resizing module to make it more suitable for the non-uniform deblurring task. To validate the evaluation algorithm, we collect sharp/aberration-degraded datasets by CODE V simulation. To test the practical application performance, we built a display-capture lab setup and reconstruct a manual registering dataset. Relevant experimental comparisons and actual tests verify the effectiveness of our proposed method.
简单透镜计算成像方法为实现高质量摄影提供了一种替代途径。它将光学前端的设计简化为单个凸透镜，并将光学像差校正交付给专用的计算恢复算法。传统的单凸透镜图像恢复基于优化理论，在效率和功效方面存在一些缺点。在本文中，我们提出了一种在生成对抗网络框架下的新型递归残差组网络（RRG-GAN），用于从像差退化的模糊图像中生成清晰图像。RRG-GAN网络包括双重注意力模块、选择性内核网络模块和残差调整模块，使其更适合非均匀去模糊任务。为了验证评估算法，我们通过CODE V模拟收集了清晰/像差退化数据集。为了测试实际应用性能，我们搭建了一个显示捕获实验室装置并重建了一个手动配准数据集。相关的实验比较和实际测试验证了我们所提方法的有效性。
