Research On Regularized Deep Models For Single Image Super-resolution

Single image super-resolution reconstruction technology uses image processing and machine learning theory to generate a high-resolution image with good objective quality and visual effects from a given low-resolution image.The super-resolution images can be widely used in medical image processing,remote sensing image processing,public safety monitoring,digital entertainment,multimedia communication and other fields,and single image super-resolution has attracted widespread attention.The vigorous development of the image super-resolution model based on deep learning(the deep super-resolution model)in recent years has achieved good results which greatly surpasses the traditional image super-resolution algorithms,and has become a research hotspot of computer vision.Most of the existing deep super-resolution models focus on the reconstruction of the fidelity term of the model and ignoring the regularization constraint analysis of the deep super-resolution models.Aiming at this problem,this dissertation studies regularization for deep super-resolution models,and uses statistical priors in the image,loss of the superresolution model,the context features,and discriminator as regularization constraints to improve the performance of the model.Four new image super-resolution reconstruction algorithms or models are proposed.The research contents and main innovations of this dissertation are as follows:1.Aiming at the existing deep super-resolution model without considering the statistical priors in the input image,a joint statistical model-guided super-resolution algorithm for the deep network is proposed.The joint statistical model uses two internal statistical priors of the image:non-local similarity and local smoothness,they are described as two total variations regular term,respectively.In order to make the reconstruction result of the deep model as similar as possible to the estimated high-resolution image,the deep network reconstruction constraint is taken as the third regular term.The joint statistical model is used as a regularization constraint to guide the reconstruction of the deep model,and a convergent separated Bregman iterative algorithm is used to optimize the total variation problem.The quantitative and qualitative results on the four benchmark datasets show that the proposed algorithm has a higher peak signal-to-noise ratio than the original deep model.Compared with the existing research work,this method establishes a unified framework of the joint statistical model and deep super-resolution model,and realizes the joint solution of the traditional super-resolution algorithm and deep super-resolution model.2.Aiming at the problem of sawtooth existed in the high-magnification,such as 4x and 8x image edge reconstruction,a super-resolution reconstruction model based on residual memory network is proposed.Combine the total variation loss and the multi-scale structural similarity loss into a new joint loss function as a regularization constraint.The constraint super-resolution model generates continuous edges.The residual memory module is used as the basic module,and features are automatically selected through the threshold mechanism.The proposed method uses fewer parameters,and also achieves good objective indicators at 4x and 8x magnification.Different from the first research content,this method focuses on using the model loss to constrain the depth super-resolution model.3.Aiming at the deep super-resolution model failing to fully consider the contextual information between the features of each layer,a single image super-resolution model based on non-local multi-scale fusion is proposed.The model uses a wide activation residual module to widen the features before the activation layer,a multi-scale fusion module to fuse features on multiple scales,and a non-local network module to obtain the global features of the image,focusing on the core area of the target,and the obtained context features are used as regularization constraints of the model.The proposed model achieved the best quantitative and qualitative results on the five benchmark datasets.Compared with the second research content,this method is a super-resolution model with deeper network layers and higher reconstruction performance.Experimental comparison results on video super-resolution reconstruction,image segmentation,and target detection tasks show that the proposed model has certain advantages over other latest super-resolution models.Compared with the existing research work,this method uses the context feature constraint model reconstruction of the deep super-resolution model.4.Aiming at the problem that visual perception-oriented image reconstruction fails to balance visual perception and objective quality,a joint attention discriminator is proposed.We use dense channel attention and cross-layer attention on the original discriminator,to improve the discriminativeness of the original discriminator,and use the joint attention discriminator as a regularization constraint for the deep super-resolution model to guide the generator to produce higher objective quality reconstruction results.The experimental results combined with several generators show that the proposed joint attention discriminator can improve the reconstructional objective indicators and subjective visual effects more effectively than the original discriminator.The research contents of the first three methods are PSNR-oriented super-resolution algorithms or models.Unlike these methods,this method mainly studies the visual perception-oriented depth super-resolution model.Compared with the existing research work,this method takes into account the role of the discriminator on the generator in the deep super-resolution model.