Regularized super-resolution reconstruction of images using wavelet fusion

A regularized wavelet-based image super-resolution recon- struction approach is presented. The super-resolution image reconstruc- tion problem is an ill-posed inverse problem. Several iterative solutions have been proposed, but they are time-consuming. The suggested ap- proach avoids the computational complexity limitations of existing solu- tions. 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 implementa- tion of each of these steps. The results indicate that the proposed ap- proach 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 pub- lished iterative solutions. © 2005 Society of Photo-Optical Instrumentation Engineers.