Research On Super Resolution Reconstruction Algorithm Based On Single Frame Image

Super resolution reconstruction is aimed to recovery the original image with high fidelity as far as possible and has been widely used in medical imaging,security monitoring,geological survey,face recognition etc..How to improve the quality of image reconstruction has been a hot research topic in the field of image processing.Image reconstruction quality is closely related to the process of image acquisition,storage and transmission,so the effective image information acquisition framework plays an important role in image reconstruction.Compressed sensing is a new signal acquisition theory which breaks through the Nyquist sampling theory's limit.Most of the existing compressed sensing reconstruction algorithms are based on the sparsity of the original signal,which greatly limits the scope of image reconstruction.To solve this problem,super resolution reconstruction based on single image is studied and discussed in this paper.This paper discusses the research background and significance of the super resolution reconstruction and summarizes the research status at home and abroad,and points out the relation between super resolution reconstruction and compressive sensing.Considering the existing image reconstruction algorithm requires a priori knowledge of image sparsity,a new reconstruction algorithm is proposed in this paper.It can adaptively select the number of atoms in each iteration in the process of image reconstruction through introducing a new parameter,and use the idea of backtracking to realize the second selecting of the atoms in the support set.It overcomes the insufficient of inputing the sparse value blindly under the condition that the signal sparsity is unknown.Considering the previous image interpolation algorithm can not be very good to protect the structure and texture details of the image,an image interpolation method based on wavelet is proposed in this paper.The method performs bilinear interpolation on the decomposed high frequency subbands,and then the interpolated high frequency subband and the low-resolution image before decomposition are regarded as a whole,and finally,the inverse wavelet transform is used to reconstruct the image.The experimental results show that the method can enrich the image detail and improve the peak signal-to-noise ratio(SNR)of the reconstructed signal,and improve the reconstruction effectively.