| dc.description.abstract | In the field of video surveillance and vision detection, traditional cameras have low video quality and detection rates, owing to their low resolution. To increase the effectiveness of system applications, the super-resolution method was developed to convert low-resolution images into high-resolution images. In this paper, three super-resolution methods (Bicubic spline, APNN, and SRCNN) are compared and evaluated through two experiments to assess their performance in areas such as face and barcode recognition. The properties of each algorithm, including human vision quality, memory usage, execution time, hardware complexity, relative power consumption, and hardware resources, are also discussed. According to the experimental results, system analyzers can choose the appropriate super-resolution method for embedded system development. Our results show that SRCNN has the greatest improvement of recognition rate among the super-resolution methods. In terms of cost, the Bicubic spline method is suitable for embedded software and hardware applications due to its low cost and high speed. APNN can be applied in embedded hardware applications because of its low resource usage. Due to the high resource usage of SRCNN, it is only suitable for certain GPUs. Therefore, we use a genetic algorithm to obtain a trade-off between hardware cost and accuracy to compute an approximation for the SRCNN hardware. We found two approximated results for SRCNN: a 0.79% decrease in hardware cost and an increase of 0.000120 in average feature difference or a 12.2% decrease in hardware cost and an increase of 0.264011 in average feature difference. | en_US |