At present,although the super-resolution (SR) reconstruction algorithm based on the Convolutional Neural Network (CNN) has achieved great success,it cannot well reconstruct the high-frequency texture of the image.As a result,there exists obvious shake in local edge of the high-resolution (HR) image.We present an edge guided dual-channel CNN SR reconstruction algorithm integrated with Morphological Component Analysis (MCA).The low-resolution (LR) image to be processed is decomposed into texture part and structure part by MCA,then the texture part and the original LR image form a dual channel together,which is then input into the modified network structure to reconstruct the HR texture part.The reconstruction loss of both the HR image and HR texture are chosen simultaneously for training.As for post-processing step,we perform histogram matching between our network output and the LR input to strengthen the visual effect and apply an iterative back projection refinement to improve the PSNR.As shown in experiment results,this method with dual-channel input can restore texture details of the image,especially restore the image with rich texture.