Citation: Zhu, B.; Lv, Q.; Tan, Z.
Adaptive Multi-Scale Fusion Blind
Deblurred Generative Adversarial
Network Method for Sharpening
Image Data. Drones 2023, 7, 96.
https://doi.org/10.3390/
drones7020096
Academic Editor: Anastasios Dimou
Received: 28 December 2022
Revised: 24 January 2023
Accepted: 26 January 2023
Published: 30 January 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Adaptive Multi-Scale Fusion Blind Deblurred Generative
Adversarial Network Method for Sharpening Image Data
Baoyu Zhu
1,2,3,†
, Qunbo Lv
1,2,3,†
and Zheng Tan
1,3,
*
1
Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road,
Haidian District, Beijing 100094, China
2
School of Optoelectronics, University of Chinese Academy of Sciences, No.19(A) Yuquan Road,
Shijingshan District, Beijing 100049, China
3
Department of Key Laboratory of Computational Optical Imagine Technology, CAS,
No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China
* Correspondence: tanzheng@aircas.ac.cn; Tel.: +86-1591-063-9123
† These authors contributed equally to this work.
Abstract:
Drone and aerial remote sensing images are widely used, but their imaging environment is
complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale
fusion to extract features in order to make full use of aerial remote sensing blurred image information,
but images with different degrees of blurring use the same weights, leading to increasing errors
in the feature fusion process layer by layer. Based on the physical properties of image blurring,
this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network
(AMD-GAN), which innovatively applies the degree of image blurring to guide the adjustment of
the weights of multi-scale fusion, effectively suppressing the errors in the multi-scale fusion process
and enhancing the interpretability of the feature layer. The research work in this paper reveals the
necessity and effectiveness of a priori information on image blurring levels in image deblurring
tasks. By studying and exploring the image blurring levels, the network model focuses more on the
basic physical features of image blurring. Meanwhile, this paper proposes an image blurring degree
description model, which can effectively represent the blurring degree of aerial remote sensing images.
The comparison experiments show that the algorithm in this paper can effectively recover images
with different degrees of blur, obtain high-quality images with clear texture details, outperform the
comparison algorithm in both qualitative and quantitative evaluation, and can effectively improve
the object detection performance of blurred aerial remote sensing images. Moreover, the average
PSNR of this paper’s algorithm tested on the publicly available dataset RealBlur-R reached 41.02 dB,
surpassing the latest SOTA algorithm.
Keywords:
drone and aerial remote sensing; image deblurring; generative adversarial networks;
multi-scale; image blur level; object detection; deep learning
1. Introduction
Drone and aerial remote sensing images have been widely used in many fields such
as land and mineral resource management and monitoring, traffic and road network safety
monitoring, and geological disaster early warning system and national defense system
construction [
1
–
5
]. However, as the geometric resolution of aerospace optical cameras
continues to increase, high-speed spatial motion and random vibration of the camera
platform can cause image shift blurring [
6
,
7
], and rapid motion of the target can create
additional blurring [
8
]. In addition, factors such as the spatially varying characteristics
of the blur kernel, the imaging depth of field, and detector noise can also increase the
complexity of image blurring to varying degrees [
9
], resulting in degraded image quality.
This not only severely affects the visualization of the images and reduces the perceptual
Drones 2023, 7, 96. https://doi.org/10.3390/drones7020096 https://www.mdpi.com/journal/drones
