Research Of The Techniques Of Super-resolution Reconstruction Of Aviation Images

With the rapid development of aviation technology, aviation imaging technology has beenwidely applied in such fields as topographic mapping, land and forest resources survey, urbanconstruction, railway and highway construction, military reconnaissance, and so on. But in aerialphotography, the image quality, which is influenced by many factors such as intensity of fogcaused by sunlight scattering, vibration of aircraft, image motion in exposure, aberration ofobjective lens in imaging system, performance of photographic materials, flight attitude and so on,is inferior. Therefore, it is of great significance in application to study super-resolutionreconstruction of aviation images aiming at the improvement of its resolution from the perspectiveof image processing. The paper puts emphasis on some of the crucial technical issues onsuper-resolution reconstruction of aviation images, the research results of which are as follows:(1) Single-frame image super-Resolution reconstructionThe capture of multi-frame images at the same scene is not available, thus, the paper doesresearch on the four algorithms for polynomial interpolation: linear interpolation, cubic B-splineinterpolation, O-MOMS interpolation and Keys interpolation, and presents four linearspace-invariant formula of polynomial interpolation. However, linear space-invariant interpolationtends to smooth edges. In order to solve the problem, the paper proposes adaptive linearspace-variant interpolation, which is an improvement of warped-distance interpolation. The pixelsin polynomial interpolation are assigned with different weights, and the weights depend on theasymmetry in neighboring pixels. The homogeneity in images yielded by weights contributes tothe improvement of the quality of reconstructed images.(2) Sequence image super-resolution reconstructionMutual information registration is applied to registration of aviation images to improve theaccuracy and robustness of image registration.In consideration of a huge mass of data in aviation images, a super-resolution reconstructionalgorithm based on a priori information is presented in the paper. The algorithm breaks thesolution into image restoration and fusion consecutively to improve efficiency. It usesnon-iterative algorithm to restore images, and then fuses redundant information andcomplementary information in images into one-frame by wavelet transform, and last reconstructs a high-resolution image by polynomial interpolation. The essence of the algorithm is deblurring.The degradation matrix is the one of block Toeplitz-To-block circulant, thus there is a non-singularmatrix to achieve diagonalization property, avoiding direct inversion of a large sparse matrix andimproving the computational efficiency. The three non-iterative algorithms for deblurring, whichare linear minimum mean square error, the maximum entropy, and regularization methodrespectively, are presented in the paper.In view that the estimation of blur kernel in aviation images is not always available, whichleads to failure to establish a precise observation model, the reconstruction algorithm of blindsuper-resolution based on two-dimensional greatest common divisor(2D-GCD) is proposed in thepaper. The algorithm, based on blind estimation of blur kernel, breaks the solution into imagerestoration and fusion consecutively. As to the blind restoration of images, the algorithm of blindrestoration by2D-GCD is employed in the paper. The2D-GCD algorithm is only to benefit fromtwo observations rather than all observations. Therefore, a new observation image incorporatingthe information from all the observations is generated, and The2D-GCD algorithm of sequenceimage is presented in the paper.(3) Spatial resolution of the sub-pixel imaging system can be improved by an increase oftemporal and spatial sampling frequency in detectors. However, there exists aliasing in datacollected by detectors and blurring in an image with high-resolution, as a result of which theresolution is far away from the ideal value. To resolve the problem, an algorithm ofsuper-resolution reconstruction concerning multi-linear array sub-pixel imaging is put forward inthe paper. First of all, an interpolation model on high-resolution grid is established. Next, blurkernels in an image with high-resolution are identified in linear array and scanning directionrespectively, from which the blur kernel in a frame is obtained. Last, a model of gradientsmoothing regularization with Neumman boundary conditions is employed to deblur.