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SR4RS 2024 : Super-resolution for remote sensing (Springer, 2024)


When Oct 2, 2023 - Oct 1, 2024
Where N/A
Abstract Registration Due Oct 2, 2023
Submission Deadline Dec 15, 2023
Notification Due Jan 15, 2024
Final Version Due Feb 16, 2024
Categories    super-resolution   remote sensing   image processing   machine learning

Call For Papers

Dear Colleagues,

We would like to invite you to contribute a chapter for the upcoming volume entitled "Super-resolution for remote sensing" to be published by Springer Nature. The volume will be available both in print and in ebook format by fall of 2024 on SpringerLink. Below is a short description of the volume:

Even though the computer vision field has recently taken great leaps, the capabilities of many systems are limited due to insufficient spatial resolution of the available input images. This is particularly relevant for remote sensing (including satellite images) when it is often very costly or even impossible to acquire data of sufficient resolution. This obstacle has motivated many researchers to explore the topic of super-resolution – a common term for a variety of techniques whose goal is to reconstruct a high-resolution image from a low-resolution observation. The emerging techniques underpinned with deep learning allow for obtaining very promising results both in terms of generating visually plausible outcomes, as well as concerning the capabilities of reconstructing the actual high-resolution information. New research directions include in particular real-world super-resolution aimed at enhancing the original (rather than simulated) images, as well as new evaluation procedures that would allow for assessing the quality of super-resolved images and understanding their value in different contexts.

This volume is aimed at presenting the state-of-the-art in super-resolution for remote sensing. Topics of interest include:
- Evaluation procedures to assess the super-resolution quality,
- Benchmark datasets (simulated and real-life),
- Super-resolution for specific data modalities (panchromatic, multispectral and hyperspectral images),
- Single-image super-resolution, including generative adversarial networks,
- Multi-image fusion (temporal and/or spectral),
- Real-world super-resolution,
- Task-driven super-resolution.

Each contributed chapter is expected to present a novel research study, a comparative study, or a survey of the literature. There will be no publication fees for accepted chapters.

Important Dates
Submission of abstracts October 02, 2023
Notification of initial editorial decisions October 16, 2023
Submission of full-length chapters December 15, 2023
Notification of final editorial decisions January 15, 2024
Submission of revised chapters February 16, 2024

All submissions should be done via EasyChair:
Original artwork and a signed copyright release form will be required for all
accepted chapters. For author instructions, please visit:

Feel free to contact us via email (michal DOT kawulok AT polsl DOT pl, jolanta DOT kawulok AT polsl DOT pl, bogdan DOT smolka AT polsl DOT pl, ecelebi AT uca DOT edu) regarding your chapter ideas.

Michal Kawulok, Jolanta Kawulok, Bogdan Smolka, and M. Emre Celebi

Michal Kawulok, D.Sc.
Associate Professor
Department of Algorithmics and Software
Silesian University of Technology
Gliwice, Poland

Jolanta Kawulok, Ph.D.
Assistant Professor
Department of Algorithmics and Software
Silesian University of Technology
Gliwice, Poland

Bogdan Smolka, D.Sc.
Department of Data Science and Engineering
Silesian University of Technology
Gliwice, Poland

M. Emre Celebi, Ph.D.
Professor and Chair
Department of Computer Science and Engineering
College of Natural Sciences and Mathematics
University of Central Arkansas
Conway, AR, USA

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