Advances in deep learning for real-time image and video reconstruction and processing

Pourya Shamsolmoali, M. Emre Celebi, Ruili Wang
2020 Journal of Real-Time Image Processing  
Accurate reconstruction algorithms play a vital role in modern imaging techniques. Reconstructing image is a central problem in many key applications including super-resolution imaging, X-ray tomography, ultrasound imaging, remote sensing, and magnetic resonance imaging. The process of image reconstruction typically requires solving an inverse problem that is ill-posed and large-scale and thus challenging to solve. The main challenge of this method is its sensitivity to measurement noise in the
more » ... input data, which will result in artifacts in the reconstructed image with a higher cost in computational time. Thus, it is very important to develop a robust method that can improve reconstruction accuracy while maintaining real-time operation. Real or near real-time processing capabilities are important in image reconstruction techniques for real-world applications. The research field of real-time image reconstruction is very active in image processing and computer vision since it proposes the ability to overcome some of the inherent resolution limitations of low-cost imaging sensors and generates better applications for the emergent capability of highresolution displays. Deep learning for image reconstruction and processing is a relatively new area. Image reconstruction based deep learning can be efficiently performed by using neural networks, in which, weights are determined based on training data. This special issue provides 20 papers reporting the recent developments of deep learning in image reconstruction.
doi:10.1007/s11554-020-01026-2 fatcat:23jzdzkoxfdnrjfeew7bpwy7fm