Multi-Frame Super-Resolution Reconstruction with Applications to Medical Imaging
This work addresses resolution limitations in various fields like medical imaging, but appears incremental as it builds on existing super-resolution techniques.
The paper tackles the problem of limited optical resolution in digital cameras by developing novel multi-frame super-resolution methods to reconstruct high-resolution images from multiple low-resolution frames, with applications demonstrated in medical imaging.
The optical resolution of a digital camera is one of its most crucial parameters with broad relevance for consumer electronics, surveillance systems, remote sensing, or medical imaging. However, resolution is physically limited by the optics and sensor characteristics. In addition, practical and economic reasons often stipulate the use of out-dated or low-cost hardware. Super-resolution is a class of retrospective techniques that aims at high-resolution imagery by means of software. Multi-frame algorithms approach this task by fusing multiple low-resolution frames to reconstruct high-resolution images. This work covers novel super-resolution methods along with new applications in medical imaging.