New Approaches and Applications of Remote Sensing Image Restoration
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".
Deadline for manuscript submissions: closed (20 May 2022) | Viewed by 5096
Special Issue Editors
Interests: remote sensing; image processing; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are seeking submissions to a Special Issue on New Approaches and Applications of Remote Sensing Image Restoration.
Remote sensing images acquisition processes are generally influenced by various kinds of degradation, such as noise, geometric distortions, changes in illumination, blur (motion, atmospheric turbulence, out-of-focus), etc. Image restoration as an inverse imaging approach is becoming one of the central issues in the development of remote sensing, since it can estimate original images from the observed distorted ones. Remote sensing images restoration can be applied as a pre-processing technique to improve image quality, which supports further stages of data analysis, object detection and classification. Besides, image restoration can be used for remote sensing data at the post-processing stage, for reducing distortions caused by lossless coding of images (blocking and ringing artifacts).
In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the field of remote sensing image restoration. Both theoretical and experimental studies are welcome, as well as comprehensive review and survey papers.
Prof. Dr. Hong Huang
Dr. Yule Duan
Guest Editors
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Keywords
- remote sensing image deblurring
- remote sensing image denoising
- remote sensing image dehazing and de-raining
- multimodal remote sensing image restoration
- multi-sensor fusion remote sensing data restoration
- deep learning for remote sensing image restoration
- sparse representation for remote sensing image restoration
- remote sensing image compression artifacts reduction
- remote sensing image classification and clustering
- remote sensing image detection
- remote sensing image feature learning
- spectral remote sensing image unmixing
- spectral remote sensing image dimensionality reduction
- remote sensing image super resolution
- remote sensing image fusion
- remote sensing image compression
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