RRG-GAN Restoring Network for Simple Lens Imaging System

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 10; p. 3317
• Main Authors: Wu, Xiaotian, Li, Jiongcheng, Zhou, Guanxing, Lü, Bo, Li, Qingqing, Yang, Hang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.05.2021; MDPI
• Subjects: Algorithms; computational imaging; Deep learning; Design; Efficiency; image restoring; Interdisciplinary subjects; Methods; Neural networks; non-uniform deblurring; Optimization; non-uniform deblurring; deep learning; image restoring; computational imaging
• ISSN: 1424-8220; 1424-8220
• 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.