Research On Industrial Data Matrix Image Restoration Algorithm Based On Sparse Representation

With the advent of the large data era,the digital multimedia technology with Internet as the core had been developed more and more rapidly and it was widely used in various fields of human society.Image signal was as the main carrier of digital multimedia,the image signal's quality was quite high in the process of transmission and application.Two-dimensional bar code as a new type of information transmission medium,to obtain useful information through the automatic identification of bar code images became more and more popular.However,due to the bar code image vulnerable to interference from external factors such as environment and human factors made the image quality degradation,eventually leading to decoding failure.Therefore,it was very important to recover the high resolution bar code images for the recognition of 2D codes as soon as possible.Image restoration has also been one of the important research topics in the field of digital image processing.The traditional idea of image restoration algorithm was to use the known information of the image to fill the unknown area according to some rules to achieve repair,efficiency of this algorithm was not high.In recent years,sparse representation theory has become a hot research topic because of its advantages of simple modeling,high robustness and strong anti-interference ability.It has important research value and significance in image processing field.The application of sparse theory to image restoration has become a new research direction in the field of image processing.For this reason,this thesis presented some new methods to solve the problem of industrial bar code image restoration based on the theory of sparse representation.The main innovations were as follows:1.This thesis studies the basic theoretical knowledge of image restoration based on sparse representation.First of all,the image restoration and two-dimensional code technology research status and the basic concepts of signal sparse representation were summarized.Secondly,the existing image restoration technologies based on sparse representation were introduced in detail,including the commonly used learning dictionary and sparse model coding algorithm.2.For unrecognized industrial Data Matrix images of some pixels that were missing or have scratched,this thesis proposed a sparse K-SVD dictionary algorithm to repair it.Based on the K-SVD image restoration algorithm,an improved sparse K-SVD dictionary repair algorithm was proposed.By adding constraints to further thinning the learning dictionary,the computational complexity of the algorithm was reduced and the sparseness of the image was enhanced.The simulation results showed that,this algorithm fixed higher image quality comparing with the existing MOD,DCT and K-SVD dictionary repair algorithms.3.For Data Matrix code was covered by parts or other objects in the industrial environment that lead to decoding incorrectly.This thesis presents an image restoration algorithm based on block clustering On the basis of sparse representation model combined with the idea of clustering.The local sparseness and nonlocal similarity of the image were fully utilized by segmenting the image in fixed overlapping pixels and clustering the image blocks into groups according to the Euclidean distance.And then an adaptive fast learning dictionary was obtained by singular value decomposition of the estimation of each group,thereby improving the computational efficiency of the algorithm learning dictionary.4.Based on the robust sparse representation model,an image restoration algorithm based on L1 norm reconstruction was studied in this thesis.After obtaining the adaptive learning dictionary,the problem of L1 norm minimization of the group sparse representation model was solved by using the separation iteration and the optimization gradient algorithm,which improves the robustness of the repair algorithm.The experimental results showed that the proposed algorithm can repair the damaged DM image with different degree of structural detail and rich information.Improving the accuracy of the algorithm and getting a better restoration effect.5.In order to further verify the effectiveness of the restoration algorithm,before and after the repair of the DM code image were decoded respectively,the results showed that the damaged DM image can be decoded correctly after repairing,so the study had a certain practical application value.