Abstract
Nowadays, people might need super resolution to obtain high quality images. Super resolution algorithm enhances high frequency information (texture or edges) to improve the image quality. We can do more things with super resolution, such as road surveillance system. The image quality would be degraded by illumination, angle, distance, and other conditions, and it will result in failing to recognize license plate or human face. Interpolation is a traditional method for super resolution, but this method does not ignore high frequency information. Therefore, example-based super-resolution methods were proposed. Besides, deep learning has great performance in many applications. However, for super-resolution, the computational complexity via deep learning algorithm is high and it needs some time to generate a high-resolution image. The time might be acceptable for single image. But what if we have to enhance video, it will take a lot of time to rebuild. Therefore, our research aims to solve this problem. We present a faster super resolution for video based on deep learning. We find the different patches between frame and frame. Add these patches into neural net and rebuild high resolution image to lower the total compute time.
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