An FPGAbased design for realtime superresolution reconstruction For several decades, the camera spatial resolution is gradually increasing with the CMOS technology evolution. The image sensors provide more and more pixels, generating new constraints for suitable optics. As an alternative, promising solutions propose superresolution (SR) techniques to reconstruct highresolution images or video without modifying the sensor architecture. However, most of the SR implementations are far from reaching realtime performance on a lowbudget hardware platform. Moreover, convincing stateoftheart studies reveal that artifacts can be observed in highly textured areas of the image. In this paper, we propose a local adaptive spatial super resolution (LASSR) method to fix this limitation. LASSR is a twostep SR method including a machine learningbased texture analysis and a fast interpolation method that performs a pixelbypixel SR. Multiple evaluations of our method are also provided using standard image metrics for quantitative evaluation and also a psychovisual assessment for a perceptual evaluation. A first FPGAbased implementation of the proposed method is then presented. It enables highquality 2xe2x80x934xc2xa0k superresolution videos to be performed at 16 fps, using only 13% of the FPGA capacity, opening the way to reach more than 60 fps by executing several parallel instances of the LASSR code on the FPGA.

WinogradBased RealTime SuperResolution System on FPGA With the rapid development of computer vision theory and visual display devices, High Frame Rate (HFR) and Ultra High Definition (UHD) techniques have received increasing attention from academic and industry. As they put high demands on performance and energyefficiency, efficient customized hardware is required. In this paper, we propose an FPGAbased superresolution system that enables realtime UHD upscaling in both high image quality and high frame rates. Our system crops each frame into blocks, measures their total variation values, and dispatches them accordingly to a neural network or an interpolation module for upscaling. We also propose a fast transposed convolution algorithm based on Winograd algorithm, which reduces the number of multiplications. Experimental results show that the proposed superresolution system achieves superior performance in both reconstruction performance and efficiency over previous works.