Study On The Super-Resolution Reconstruction Technique For Remote Sensing Image

Image super-resolution (SR) reconstruction refers to the techniques to reconstruct one or more images with high resolution (HR) from the low resolution image sequence which was taken about the same scene from different viewpoint, different time or from different sensors. Due to the influence of air turbulence, the relative motion between the object and the sensors and the influence of optical systems, the images obtained are unavoidable degraded by blurring, additive noises which decrease the spatial resolution. SR technique can be used to fuse the information contained in the image sequence to reconstruct a HR image with higher quality. The process of SR reconstruction can be seen as the following steps: image sequence registration, interpolation, blur function identification and reconstruction algorithm. In this thesis, the author did some work on the following problems: image registration, image restoration, image reconstruction under different noise model, remote sensing image SR reconstruction algorithm.Firstly, the image registration problem was discussed and a new registration algorithm based on the Powell optimal algorithm and the image's pyramid decomposition was proposed to estimate the registration parameters. The images were decomposed using Gaussian pyramid method and then the initial registration parameters were initialized at the bottom level, then the Powell algorithm was used on the object function to estimate the parameters in this level. The estimated values were then used at another level as the initialized values to estimate the new parameters. This process was carried through at the finest level and the parameters were estimated finally. Numerical experiment shows that this algorithm can estimate the predetermined parameters with higher accuracy.Then, the blur function was discussed and after that an image restoration algorithm based on adaptive neural network was given. When the blur function was known, this algorithm could give better restoration results. This thesis also discussed the SR reconstruction algorithm when the LR images were degraded by different noise models such as Poisson noise and impulse noise. For different noise model, the reconstruction formulas were given and numerical experiments were made to show the performance of these algorithms.In this thesis the wavelet transform method was discussed and the SR algorithm based on the wavelet theory was given. Multi-channel SR algorithm was presented and the numerical experiments were given. Based on the statistics model of wavelet coefficients, the modified SR algorithm was proposed.Finally, this thesis discussed the problem of remote sensing image SR reconstruction. The panchromatic image in ETM+ was used to reconstruct the multi-spectral images. The reconstruction model was established and the parameter estimation algorithms were discussed. A new adaptive regularization parameters estimation algorithm based on the local variance of the images was proposed. The observed multi-spectral images were seen as the low resolution image sequences which were the degraded versions of the corresponding multi-spectral images with higher spatial resolution. The degradation process was blurring transform, under sample and additive noise. The panchromatic image was seen as the high resolution observed image which can be seen as the combination of unknown multi-spectral high resolution images. The task of SR reconstruction for remote sensing images was to merge the spatial information of the panchromatic image into the multi-spectral images. Numerical experiments were made to verify the performance of this algorithm and the reconstructed results were compared with that of principal component analysis fusion algorithm and wavelet transform fusion algorithm which demonstrate validity of the proposed algorithm. Finally, the uncertainty problem of SR reconstruction was discussed using some examples.