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Regularized super-resolution reconstruction of images using wavelet fusion

机译：Regularized super-resolution reconstruction of images using wavelet fusion

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摘要

A regularized wavelet-based image super-resolution reconstruction approach is presented. The super-resolution image reconstruction problem is an ill-posed inverse problem. Several iterative solutions have been proposed, but they are time-consuming. The suggested approach avoids the computational complexity limitations of existing solutions. It is based on breaking the problem into four consecutive steps: a registration step, a multichannel regularized restoration step, a wavelet-based image fusion and denoising step, and finally a regularized image interpolation step. The objective of the wavelet fusion step is to integrate all of the data obtained from the multichannel restoration step into a single image. The wavelet denoising is performed for the low-SNR cases to reduce the noise effect. The obtained image is then interpolated using a regularized interpolation scheme. The paper explains the implementation of each of these steps. The results indicate that the proposed approach has succeeded in obtaining a high-resolution image from multiple degraded observations with a high peak SNR. The performance of the proposed approach is also investigated for degraded observations with different SNRs. The proposed approach can be implemented for large-dimension low-resolution images, which is not possible in most published iterative solutions.

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