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Forensics facial images are usually provided by surveillance cameras and are therefore of poor quality and resolution. Simple upsampling algorithms can not produce artifact-free higher resolution images from such low-resolution (LR) images. To deal with that, reconstruction-based super-resolution (SR) algorithms might be used. But, the problem with these algorithms is that they mostly require motion...
Feeding low-resolution and low-quality images, from inexpensive surveillance cameras, to systems like, e.g., face recognition, produces erroneous and unstable results. Therefore, there is a need for a mechanism to bridge the gap between on one hand low-resolution and low-quality images and on the other hand facial analysis systems. The proposed system in this paper deals with exactly this problem...
Super resolution algorithms are necessary for improving the quality of low resolution video sequences from surveillance cameras. These algorithms have two main problems: first, they hardly can improve the quality of their inputs by factors bigger than two. Second, applying them to real video sequences usually produces unstable and noisy output. The proposed system in this paper deals with these two...
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