Remote Sensing Image Denoising, Restoration and Reconstruction
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 51421
Special Issue Editors
Interests: computational imaging; compressed sensing; efficient signal processing algorithms; image/video restoration and compression
Special Issues, Collections and Topics in MDPI journals
Interests: statistical image modeling; sparse representation; image restoration and reconstruction; analysis of high-dimensional data; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: multichannel remote sensing; image processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
During image acquisition process remote sensing images are corrupted by various kinds of degradations, such as noise, geometric distortions, changes in illumination, blur (motion, atmospheric turbulence, out-of-focus), etc. Image restoration/reconstruction (IR) is an inverse imaging problem aiming at estimating original images from the observed distorted ones. IR can be applied on a sensor data at the pre-processing stage, to improve image quality and to support further stages of data analysis, object detection and classification, or at the post-processing stage, to reduce distortions caused by lossless coding of images (blocking and ringing artifacts).
This Special Issue will present recent advances in inverse imaging of remote sensing data. Specifically, novel model-based, machine learning methods, or hybrid methods of image restoration, image denoising, deblurring (blind and non-blind), image super-resolution will be of special attention.
Topics of interest include but are not limited to:
- Image denoising
- Image deblurring (blind and non-blind)
- Image super-resolution
- Image dehazing and de-raining
- Image compression artifacts reduction
- The effect of image restoration on clustering, classification and target detection
- Sparse representation and low-rank approximation for image restoration in remote sensing
- Deep learning models for image restoration, with emphasis on robustness to adversarial attacks and data variation
- Multimodal image restoration and joint restoration and fusion of multi-sensor data
Prof. Dr. Karen Egiazarian
Prof. Dr. Aleksandra Pizurica
Prof. Dr. Vladimir Lukin
Guest Editors
Manuscript Submission Information
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Keywords
- Image denoising and enhancement
- Image deblurring (blind and non-blind)
- Image super-resolution
- Image dehazing and de-raining
- Image compression artifacts reduction
- Restoration of multi-modal images and multi-sensor data
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