Super-resolution video generation algorithm for surveillance applications

Abstract

Video surveillance is one of the major applications where high-resolution (HR) images are crucial. Since the video camera has limited spatial and temporal resolution, there is a need for super resolution video generation algorithms. In this paper, we have presented a novel technique for activity detection in the surveillance video. To achieve this goal, we have proposed and investigated efficient algorithms for Video Object Plane (VOP) generation, shadow removal from VOP and super-resolved VOP generation, for activity detection from surveillance video. The proposed VOP generation algorithm is computationally efficient and works for both dynamic and static backgrounds. The novel shadow removal algorithm for the VOP is based on texture and its performance has been studied based on average shadow detection and discrimination rates. The proposed super-resolution video generation algorithm has been designed using edge models. The performance of this algorithm has been evaluated using a numerical analysis technique and is found to be better than bi-cubic and bi-linear interpolation techniques.

Residual scale attention network for arbitrary scale image super-resolution

“…I MAGE super-resolution (SR) is an effective technique to construct a high-definition (HD) image with finer details from one or multiple low-resolution images. The past two decades have witnessed the bloom of image SR algorithms with a broad of applications including video surveillance [1], video streaming [2], [3], free-viewpoint television [4], [5], etc. However, how to reasonably evaluate the quality of SR images to compare and optimize SR methods remains challenging.…”

A Comparable Study of CNN-Based Single Image Super-Resolution for Space-Based Imaging Sensors

“…HR images have higher pixel densities and finer details than LR images. Image SR has been proved to be of great significance in many applications, such as video surveillance [21,22,23], ultra-high definition TV [24], low-resolution face recognition [25,26,27,28,29] and remote sensing imaging [30,31]. Benefiting from its broad application prospects, SR has attracted huge interest, and currently is one of the most active research topics in image processing and computer vision.…”