Preprint Article Version 1 This version is not peer-reviewed

A Sensor Image Super-Resolution via Advanced Generative Adversarial Network

Version 1 : Received: 15 November 2018 / Approved: 16 November 2018 / Online: 16 November 2018 (10:44:26 CET)

How to cite: Wang, S.; He, L.; Bi, D.; Wang, C. A Sensor Image Super-Resolution via Advanced Generative Adversarial Network. Preprints 2018, 2018110400 (doi: 10.20944/preprints201811.0400.v1). Wang, S.; He, L.; Bi, D.; Wang, C. A Sensor Image Super-Resolution via Advanced Generative Adversarial Network. Preprints 2018, 2018110400 (doi: 10.20944/preprints201811.0400.v1).

Abstract

Complementary Metal-Oxide-Semiconductor (CMOS) is a typical image sensor that has a wide range of applications. However, considering the limitations of the weather condition and hardware cost, it is hard to capture high-resolution images by CMOS sensor. Recently, Super-Resolution (SR) techniques for image restoration has been gaining attentions due to its excellent performance. Under the powerful learning ability, Generative Adversarial Networks (GANs) have been proved to achieve great success. In this paper, we propose the Advanced Generative Adversarial Networks (AGAN) to efficiently correct these issues; 1) we design a Laplacian pyramid framework as pre-trained module, which is beneficial to provide multi-scale features for our input. 2) at each feature block, a convolutional skip-connections network, which may contain some latent information, is significant for generative model to reconstruct a plausible-looking image; 3) considering that edge details usually play an important role in image generation, a novel perceptual loss function is defined to train and seek optimal parameters. It is effective to achieve excellent and compelling quality captured by CMOS sensor. Quantitative and qualitative evaluations have been demonstrated that our algorithm not only fully takes advantage of Convolutional Neural Networks (CNNs) to improve the image quality, but also performs better than previous GAN algorithms for super-resolution task.

Subject Areas

Super-Resolution; Deep-learning; Generative Adversarial Networks; CMOS sensors

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