Large field of view infrared images have low resolution and few high-frequency details for its imaging characteristics. So, the super-resolution reconstruction algorithms that has good results in visible image may not be suitable. Therefore, it is important to study the super-resolution of large field of view infrared images. Based on convolutional auto-encoders, a residual sparse Auto-encoders was constructed. The residual sparse auto-encoders was composed of ten convolution layers, two maxpooling layers and two upsampling layers. Consulting the residual theory, the first layer was added to the last layer and the second layer was added to the second to last layer. To achieve a better result, the spare was also added in the model. In order to analyzed the result of super-resolution, the classical algorithms of Bicubic and SRCNN were discussed. With the image and the data, it can be showed that result of the residual sparse Auto-encoders was better than the Bicubic and SRCNN.
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