Multi-Frame Super-Resolution Reconstruction with Applications to Medical Imaging
원문 초록
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.
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MFSR-GAN: Multi-Frame Super-Resolution with Handheld Motion Modeling
TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With Transformers
Multi-Frame Super-Resolution Combining Demons Registration and Regularized Bayesian Reconstruction
IM-LUT: Interpolation Mixing Look-Up Tables for Image Super-Resolution
PanoDiff-SR: Synthesizing Dental Panoramic Radiographs using Diffusion and Super-resolution
HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation
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