UAV Image High Fidelity Compression Algorithm Based on Generative Adversarial Networks Under Complex Disaster Conditions

Abstract

This paper proposes an improved image high fidelity compression algorithm based on the generative adversarial networks (GANs) to deal with the problem that the UAV image has a large amount of data which is not conducive to post-processing. By adding an encoder in front of the generator, the disaster area image transmitted by UAV is compressed to meet the requirements of the generator. After the compressed image is trained together with the real image through the discriminator, the quality of the compressed image is constantly improved. This image compression algorithm can fully synthesize the codes of non-major areas such as trees and rivers in the image, and try to retain the codes of important areas such as houses and roads. The experimental results show that the proposed compression method in this paper has a higher compression ratio than the traditional compression method for the disaster area image, and can obtain images with strong sense of hierarchy. INDEX TERMS Complex disaster, convolutional neural networks, discriminator, generative adversarial networks, generator, high fidelity, image compression.

Spectrum of Advancements and Developments in Multidisciplinary Domains for Generative Adversarial Networks (GANs)

“…Wang et al [27] 2018 Noise filtering to avoid information loss during remote sending Benefit: image de-noising Qiuhong et al [28] 2019 High volume data redundancy by applying compression NLP-natural language processing (NLP) Li et al [29] 2018 Text regression model for association of text data and social outcome Advantage: data analysis with limited labelling Lin et al [30] 2017 Rank-Gan for data analysis and quality assessment using rank metric Qian et al [31] 2018 Event factuality identification using Ac-GAN by learning syntactic inform and address imbalance among factuality values Advantage: reduced reliance over annotated text Health Che et al [32] 2017 Hergan for synthetic health data generation with limited electronic health record (HER) Hwang et al [33] 2017 Disease prediction using AC-GAN and stacked auto-encoder Rezaei et al [34] 2018 Semantic segmentation and disease classification by selective weighted loss Advantage: address Data imbalance Fake audio, video and image generation Choi et al [35] 2018 Stargan for fake image generation by using deep CNN Achieved high classification accuracy Nataraj et al [36] 2019 Detection of fake images using co-occurrence matrices along with deep learning Achieved good generalization and very high classification accuracy Agriculture Suarez et al [37] 2017 Strength assessment of vegetation against normalized difference vegetation index (NDI) by applying Conditional GAN Barth et al [38] 2017 Cyclegan for gap reduction between synthetic and empirical image data set Advantage: ease of translation of color and textures Music Yang et al [39] 2017 Midinet-generation of musical notes by using CNN GAN Comparison of midinet was also made with Google's melodyrnn from scratch Advantage: combine existing melodies as well as generate melodies from multiple channels [40] 2018 Misegan-generates symbolic music i.e. piano-rolls of five tracks and four bars i.e.…”

“…The occurrence of hefty volume of data results in problems during post processing phase. In [28] the authors try to overcome this issue through the use of compression techniques to compress the image taken by UAV. This compression is done by an encoder which is placed in front of the generator whereas to improve the quality of compressed image, discriminator is used.…”

Image Text Deblurring Method Based on Generative Adversarial Network

“…In recent years, with the deep prospect of deep learning in the areas of image semantic repair and image compression [14], more and more scholars have discovered that the work that neural networks can cover is far more than just estimating fuzzy kernels. In 2017, Nah et al [15] proposed the use of multiscale convolutional neural networks to directly deblur images.…”

SlimRGBD: A Geographic Information Photography Noise Reduction System for Aerial Remote Sensing

“…In fact, the data set was built during the denoising network training. In each training epoch, the combination of x j and v k is changed and a new dataset X, Y ' is gotten, which results data increase in next step [30]. which is the difference between the input and the potentially clean image.…”