Gradient Image Super-resolution for Low-resolution Image Recognition

In visual object recognition problems essential to surveillance and navigation problems in a variety of military and civilian use cases, low-resolution and low-quality images present great challenges to this problem. Recent advancements in deep learning based methods like EDSR/VDSR have boosted pixel domain image super-resolution (SR) performances significantly in terms of signal to noise ratio(SNR)/ mean square error(MSE) metrics of the super-resolved image. However, these pixel domain signal quality metrics may not directly correlate to the machine vision tasks like key points detection and object recognition. In this work, we develop a machine vision tasks-friendly super-resolution technique which enhances the gradient images and associated features from the low-resolution images that benefit the high level machine vision tasks. Here, a residual learning deep neural network based gradient image super-resolution solution is developed with scale space adaptive network depth, and simulation results demonstrate the performance gains in both gradient image quality as well as key points repeatability.