RRG-GAN Restoring Network for Simple Lens Imaging System

Code for this paper

Xiaotian WU, Jiongcheng Li, Guanxing Zhou, Bo LÜ, Qingqing Li and Hang Yang

Overview

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.

Architecture

Source Code coming soon.

Datasets

The datasets for training can be downloaded via the links below:

• 200-sharpimg-fromMITK5-Code:1234
• 200-blurryimg-simuByCodeV-Code:1234