Research And Implementation Of Depth Image Super Resolution Reconstruction

In practical life,because of the limitation of hardware technology,the users often can not get the appropriate image,which not only affects the user's visual enjoyment,but also hinders the present computer vision application.Therefore,it is very important to improve the image resolution from software.Super-resolution Reconstruction is the main purpose of enhancing image information.Since the theory was put forward,it has been widely used in medical image processing,biology Feature recognition,military and civilian remote control and other fields,so to improve the depth of the image resolution is particularly important.At present,super-resolution reconstruction algorithm based on depth image can be divided into:super-resolution reconstruction using depth image and fusion image super-resolution reconstruction of scene color image.Only the depth image is used for super-resolution reconstruction.In this thesis,the SRR based on sparse representation is researched,and the SRR is introduced into the low-resolution depth image by the theoretical model of CS based on the theory of Compressive Sensing.Firstly,sparse representation of low resolution depth image is presented by sparse matrix.Then,combining with the advantages and disadvantages of OMP algorithm and weak selection ROMP algorithm,the adaptive weight reduction algorithm running time is introduced and the reconstructed image effect is improved.Because SRR based on CS theory lacks a priori knowledge of the image,the SRR is trained by the K-SVD algorithm to train the redundant sparse dictionary.In the K-SVD dictionary training process,lack of consideration of the mobility between the sample,the use of classification dictionary training method,and through adaptive weak selection ROMP algorithm for dictionary training,reduce dictionary training time,the reconstruction algorithm TV algorithm The reconstructed image has a more obvious texture edge than the original K-SVD algorithm.Depth image super-resolution reconstruction algorithm for scene color image fusion,which includes adaptive weight filtering method,joint bilateral filtering method and so on.Because K-means algorithm can cluster the color image quickly and efficiently,the RGB image information in the same scene is clustered by this algorithm,and the mathematical model of RGB image information and Depth image information is built.Depth image corresponding weight.Finally,the reconstructed image with high resolution is reconstructed by corresponding weights.