  Image Super Resolution (SR) finds applications in areas where images need to
be closely inspected by the observer to extract enhanced information. One such
focused application is an offline forensic analysis of surveillance feeds. Due
to the limitations of camera hardware, camera pose, limited bandwidth, varying
illumination conditions, and occlusions, the quality of the surveillance feed
is significantly degraded at times, thereby compromising monitoring of
behavior, activities, and other sporadic information in the scene. For the
proposed research work, we have inspected the effectiveness of four
conventional yet effective SR algorithms and three deep learning-based SR
algorithms to seek the finest method that executes well in a surveillance
environment with limited training data op-tions. These algorithms generate an
enhanced resolution output image from a sin-gle low-resolution (LR) input
image. For performance analysis, a subset of 220 images from six surveillance
datasets has been used, consisting of individuals with varying distances from
the camera, changing illumination conditions, and complex backgrounds. The
performance of these algorithms has been evaluated and compared using both
qualitative and quantitative metrics. These SR algo-rithms have also been
compared based on face detection accuracy. By analyzing and comparing the
performance of all the algorithms, a Convolutional Neural Network (CNN) based
SR technique using an external dictionary proved to be best by achieving robust
face detection accuracy and scoring optimal quantitative metric results under
different surveillance conditions. This is because the CNN layers progressively
learn more complex features using an external dictionary.
