Research On The Superresolution Image Reconstruction Technique In Space Domain

The superresolution image reconstruction technique can comprehensively utilize the mutual information existing in the multiple discrete images, multiple video sequences, or between the single image and the training images to reconstruct the new image data with better quality and higher resolution, which can well make up the spatial resolution shortcomings of current image data and efficiently enhance the image spatial resolvability and definition. The superresolution image technique has a wide application in the military remote sensing and reconnaissance, target recognition and location, astronomical observation, police anti-crime detection, traffic surveillance and etc. The investigation on the technique has an important theoretical and practical meaning.In the thesis systematic investigation and experimentation is carried out on the space domain superresolution image reconstruction technique. The study focus is put on the multiple discrete image reconstruction technique, the video sequences reconstruction technique and the single image reconstruction technique these three developing directions. The main work and innovations are listed as follows:1.An explanation is given on the concept, connotation and research background of the superresolution image reconstruction technique. It is concluded that the superesolution image reconstruction technique can be divided into three study directions, which are the superresolution reconstruction techniques for the multiple discrete images, the video sequences, and the single image respectively. Detailed analysis and summary is given on the developing history, study actuality and existed problems of each direction.2.Based upon the detailed analysis on the optical flow estimation approach properties, a optical flow method with the motion oriented smoothness constraint and a multi-stages optical flow algorithm based on pyramids are proposed, which can enhance the accurateness and reliability of motion estimation results and avoid the reconstruction image details loss caused by the over-smoothed motion edges in the case of small geometric distortions.3.Based on the comparison and discussion of various block motion search strategies, a block motion algorithm based on the dual-cross refining search strategy is brought forward. The new search strategy can effectively advance the matching speed, and ensure the accuracy and reliability of motion estimation results under the condition of large geometric distortions.4. An investigation is made on the maximum a posteriori (MAP) algorithm. The prior model modeling problem and the MAP objective function parameter estimation problem are noted and investigated for the first time in the thesis. The approximate PSF examining (APEX) algorithm is introduced to obtain the initial reconstruction image as the foundation for the prior image model and the maximum likelihood (ML) scheme is devised to estimate the unknown parameters in MAP objective function. Moreover, detailed analysis is given on the properties of the unknown parameters. Thus the improved MAP algorithm with the additional parameter estimation is formed based upon the above work. Finally, a whole expectation-maximization optimization flow is devised for the improved MAP algorithm. The experimental results of the simulation images, real remote sensing images and video images demonstrate that the improved MAP algorithm can significantly increase the image spatial resolution and enhance the edge textures, which is especially notable for the remote sensing images.5.Based upon the detailed analysis on the projection onto convex sets (POCS) superresolution reconstruction algorithm, an improved POCS algorithm is put forward. The innovations include the introduction of the APEX blind deconvolution algorithm to provide the initial high resolution reconstruction image, the design of the non-decreasing energy constraint and the corresponding projection operator, and the adoption of the Butterworth low-pass filter to weaken the edge ringing effect in the POCS reconstructed image. The experimental results prove the improved POCS algorithm is capable of effectually enhancing the spatial resolution and strengthening the texture character information in the input images. Its reconstruction performance is especially notable for the compressed surveillance video with little information.6. A video sequences superresolution reconstruction approach is investigated and realized. The main work include the motion blur source analysis, the video sequence imaging model construction, the multiple video sequences registration, the video sequence reconstruction objective function design, and the video sequence reconstruction flow devision. The experiments for the real video sequences show that the video sequence reconstruction technique can well enhance the spatial resolution of input videos.7.A kind of single image superresolution reconstruction algorithm, named as the IRecogstruction method, is put forward through the adoption of the pattern recognition approach. Three innovations are adopted in the Irecogstruction method. Firstly, the binary finite orthogonal wavelet transform is introduced to fully utilize the image spatial scale information and direction information to construct the recognition features. Secondly, the local best matching algorithm is proposed to search the corresponding recognition feature, greatly decreasing the computation and complexity of the full search. Thirdly, a new concept, the best matching traning image, and its construction approach are put forward, which can facilitate the subsequent object function modeling problem and the optimization flow. The experiments on the the real video surveillance images prove the effectiveness of the IRecogstruction algorithm.