Research On Reconstruction Technology For Degraded Image

In image communications, the degraded image reconstruction is usually used to analyze and process the images.The high quality image is basically used in two application areas.Firstly,image information is clearer shown.Secondly, the image is more impressively recognized. With the development of the information society, owing to the need for high resolution images and high quality transmission,the degraded imagereconstruction develops faster and faster.Aiming at the interpolation of super-resolution algorithm,this paper uses four different image interpolation algorithms:Bicubic,NEDI,ICBI,Curvature Based Edge-Preserving(CBEP). The paper also uses a variety of models by resuming the fundamental principles of interpolation algorithms. Aiming at super-resolutionmainly for the dictionary-basedsparse representation,this paper briefly introduces the operational principle. At last, this paper puts them into super-resolution reconstruction and carries out experiments, and then obtains the experimental results.The results show that among all the four interpolation algorithms,the CBEP method with directional parameters can do more comprehensive estimation because of its multi-directional collection.It can alleviate the deviation of edge processing and be more suitable to images with the weak boundary and generous texture region than the others.At present, as the growth of the user demand for high definition video and the wide application of wireless video transmission, the video coding standards based on block is widely used in multimedia technology. However, when video stream is transmitted over error-prone channels, packet loss will lead to severe quality reduction. A simple method to correct error is to retransmit the lost data. However, in many cases, retransmission is impossible due to real-time constraints or lack of bandwidth. Therefore, we devote ourselves to study error concealment(EC) techniques to guarantee the quality of the received videos within limited transmission conditions. EC, as a post processing method, recovers the missing blocks without modifying the encoder or channel coding schemes.The basic idea of EC is to estimate the corrupted pixels using correctly received ones in current image or adjacent frames based on strong correlations within images or videos.To improve the spatial error concealment(SEC) for consecutive block loss, an edge-aware spatial-frequency extrapolation(ESFE) algorithm and its edge-guided parametric modelare proposed by selectively incorporating the edge synthesis intothe signal extrapolationarchitecture. The dominant edges that cross the missing blocks are firstly identified by the Canny detector, and then the robust Hough transformation is utilized to systematically connect these discontinuous edges. During the generation of edge-guided parametric model, the synthesized edges are utilized to divide the missing blocks into the structure-preserving regions, and thus the residual error is reliably reduced. By successively minimizing the weighted residual error and updating theparametric model, the known samples are approximated by a set of basic functions which are distributed in a region containing both known and unknown samples. Compared with other state-of-the-art SEC algorithms, experimental results show that the proposed ESFE algorithm can achieve better reconstruction quality for consecutive block loss whilekeeping relatively moderate computational complexity.