A Single Surveillance Image Super-Resolution Method Based on Gradient Matching
Gan Ya-li · Computer Engineering and Science · 2009
In surveillance video, the area of important objects such as faces often appears to be small, for the limits of capture conditions and devices. Interpolating images usually results in a blurring of edges and image details, then it is impossible for the tasks such as the recognition of faces. This paper presents the technique of super-resolution in surveillance scenarios, the process of which includes training and enhancing. In the training phase, we get the training data which are made up of low-frequency patches and high-frequency patches. In the enhancing phase, we construct the search vector from the low-resolution image patch and get the high resolution information from the training data through matching. In order to improve the veracity of searching, we introduce the gradient matching to construct the search vector, so we can get the most similar patches and improve the enhancing effect. The experiment shows that this method preserves fine details, such as edges, generates believable textures and gives good results.