Super-Resolution Reconstruction of Texture Image based on Mixed-Scale Non-Local Attention

Compared with ordinary images, the local detail of texture images has a small scale while high density, which may lose high-frequency details at low-resolution, thus affecting the effect of super-resolution image reconstruction. To solve this problem, we presented a super-resolution method for texture images using Mixed-Scale Non-Local Attention(MSNLA). Firstly, we proposed Equal-Scale Non-Local attention (ESNLA) based on Cross-Scale Non-Local Attention(CSNLA) to extract the high-frequency information of equal-scale similar feature blocks in the whole image. Besides, considering that deploying parallelized non-local attention modules will bring heavy computational burden and will increase the number of parameters, we proposed a parameter sharing method that combined CSNLA and ESNLA, namely MSNLA. Secondly, we fused the similar feature of different scales generated by MSNLA into the input feature map using channel projection. Finally, we combined the features extracted by MSNLA for super-resolution reconstruction using non-local feature fusion. Experimental results on Describable Texture Dataset(DTD) demonstrate that our proposed algorithm improve the PSNR by 0.15 dB while reducing the number of model parameters by about 10.3% with better visual effect.