Image super-resolution reconstruction based on multi-dictionary and edge fusion

Due to the special environment of coal mine,the images are generally blurred and have weak edges,and the image super-resolution reconstruction algorithm based on dictionary learning is usually that all blocks are reconstructed by single dictionary,ignoring the differences among them and being bad for reconstructing the mine images. Combined with the characteristics of mine images,this paper proposes a multi-dictionary learning image super-resolution method with edge fusion. In the method,all image blocks are classified according to the gradient statistics and dictionary librar- ies are trained. Finally,the image blocks reconstructed by different dictionaries are merged into a complete high resolu- tion image. In order to perfect the edge information,the preprocessing stage of the low-resolution image performs edge fusion to enhance their features. The high-resolution image reconstructed by dictionary learning uses the prior knowl- edge to fuse the edge information,thus correct the errors in the reconstruction process. The experiment shows that the effect of this method is improved compared with other super-resolution image reconstruction methods based on dictiona- ry learning,which can reconstruct the edge details of the image well and suppress the ghosting and ringing effect,and the value of average PSNR is increased by 1. 19 dB.