Deep-Learning Approaches for High Dynamic Range Sensing and Imaging
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: closed (10 May 2021) | Viewed by 6057
Special Issue Editors
Interests: computer graphics; image processing; imaging; image/video encoding; image/video quality evaluation; deep-learning; computer vision
Special Issues, Collections and Topics in MDPI journals
Interests: augmented reality; virtual reality; serious games; interactive environments; brain–computer interfaces
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
High dynamic range (HDR) imaging is a well-established technology that enables the acquisition, storage, manipulation, delivery, and evaluation of a higher dynamic range than the one available in traditional 8-bit per color channel technology (standard dynamic range (SDR) technology). This has brought a number of advantages, such as more realistic color reproduction, more details in bright and dark areas, better contrast, improved colors, etc. This has revolutionized the way we are now experiencing entertainment, as well as the way we are using images and videos in the image processing and computer vision fields. HDR imaging has finally moved from academia to the market.
We are experiencing a large use of HDR technology in the entertainment sector, and are also starting to see its use in industrial applications. On the other hand, we are assisting in a paradigm change in the image-processing area, where traditional techniques are surpassed by more flexible deep-learning-based approaches. In the last few years, we are also observing this specific trend in the HDR imaging field. This has brought a number of challenges that need to be addressed in order to make deep-learning-based HDR approaches more robust and resilient to unseen data and/or data which is too noisy.
Topics included but not limited to
Deep-learning-based techniques for images/videos for:
- High dynamic range (i.e., camera, image and vision, sensors);
- Single/multi-exposure HDR content acquisition;
- Image fusion for HDR content;
- HDR formats and standardization;
- HDR objective metrics;
- HDR de-ghosting artifacts removal;
- Tone mapping/inverse tone mapping;
- Color correction for HDR content;
- Gamut adjustment for HDR content;
- Real-time HDR applications;
- Mixed reality for HDR;
- Image-based lighting.
Dr. Alessandro Artusi
Dr. Fotis Liarokapis
Dr. Francesco Banterle
Guest Editors
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