Infrared Image Super-Resolution with Parallel Random Forest

Infrared imaging has the advantage of all-weather working ability. Due to the limitation of the hardware and the high cost, the resolution of infrared image (IR) is very low. To improve the resolution of IR images, this paper exploits super-resolution (SR) method for IR images. A new SR framework by using random forests is proposed in this paper. Existing methods adopts single regression model for SR. However, which single regression model tends to overfit training data, and would lead to a poor performance. Furthermore, the existing methods are not suitable for real-time system due to the heavy time consuming. To resolve this problem, an ensemble regression model, i.e. random forests rather than single regression model is adopted in this paper. In addition, to achieve better results multi-regression models rather than a single regression model are trained on the clustered training data. Moreover, the features used in many SR methods cannot extract features on diagonal orientation. To resolve this problem, we adopt a second order derivative filter, which can extract features on diagonal orientation. The experimental results demonstrate the availability of the proposed method.