Computational Imaging and Sensing

Dear Colleagues,

Computational imaging refers to the technique, in which optical capture methods and computational algorithms are jointly designed in the imaging system, aiming to produce images with more information. One clear feature of computational imaging is that the captured data may not actually look like an image in the conventional sense but are transformed to better suit the algorithm layer, where essential information are computed. Therefore, computational imaging senses the required information of targets and/or scenes, blurring the boundary of imaging and sensing.

Benefiting from the ever developing processor industry, computational imaging and sensing begin to play an important role in imaging techniques and have many applications in areas of smartphone photography, 3D sensing and reconstruction, biological and medical imaging, synthetic aperture radar, remote sensing and so on. A lot of recent researches have accelerated the imaging and sensing performance of those applications.

The topical collection focuses on computational imaging to obtain high-quality images and to solve problems which cannot be solved by optical capturing or post processing alone, in order to improve imaging and sensing performance of physical or optical devices in terms of image quality, imaging speed, and functionality. The purpose of this topical collection is to broadly engage the communities of optical imaging, image processing and signal sensing to provide a forum for the researchers and engineers related to this rapidly developing field and share their novel and original research regarding the topic of computational imaging and sensing. Survey papers addressing relevant topics are also welcome. Topics of interest include but are not limited to:

• Computational photography for 3D imaging;
• Depth estimation and 3D sensing;
• Biological imaging (FLIM/FPM/SIM)
• Medical imaging (CT/MRI/PET image reconstruction);
• Image restoration and denoising;
• Image registration and super-resolution imaging;
• Ghost imaging and single-pixel imaging;
• Photon counting and single-photon imaging;
• High-speed imaging systems and bandwidth reduction;
• Computational sensing for advanced driver assistance systems (ADAS);
• Synthetic aperture radar (SAR) imaging;
• Remote sensing;
• Ultrasound imaging;
• Computational sensing for advanced image signal processor (ISP);
• Deep learning for image reconstruction;
• Remote sensing and UAV image processing;
• Under-water imaging and dehazing.

Prof. Dr. Ming-Jie Sun
Prof. Dr. Jinyang Liang
Collection Editors

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