Adaptive Texture Distillation Network for Image Hybrid Super-Resolution

To save the transmission bandwidth of high-resolution (HR) images, we can send down-sampled low-resolution (LR) images and reconstruct them using super-resolution (SR) technology at the receiving end. However, image down-sampling by a large factor results in the loss of many spatial details. Instead, we use a combination of spatial down-sampling by a small factor and gray-level quantization to obtain the low hybrid-resolution images. Although the small down-sampling factor makes images retain more spatial details and real textures, the gray-level quantization introduces fake textures. Obviously, the real textures should be enhanced, and the fake textures should be eliminated. To address this issue, we propose a lightweight Adaptive Texture Distillation Network (ATDN) for image hybrid super-resolution. Our model uses the texture enhancement block (TEB) and the texture smoothing block (TSB) to handle real and fake textures in different ways. Considering that the mixing proportions of two kinds of textures in low hybrid-resolution images vary with regions, we specifically use a cascaded weight branch to adaptively adjust the weights of real and fake textures. Experiments reveal that our model can effectively deal with the mixing problem of real and fake textures, and our method can achieve superior performance to other lightweight methods.