الفهرس | Only 14 pages are availabe for public view |
Abstract In recent years, many satellites were launched, which provide various types of images about the earth surface. These images have different spectral and spatial characteristics. Super-Resolution (SR) is a fusion process for reconstructing a HR image from several lower resolution (LR) images covering the same region in the world. If the images are sub-pixel shifted, then each LR image contains different information. SR extends the classical single-frame image reconstruction/restoration methods by simultaneously utilizing information from multiple observed images to achieve resolutions higher than those of the original images. Image fusion techniques are used extensively to combine different images having complementary information into one single composite. The fused image has rich information that will improve the performance of image analysis algorithms. Pan-sharpening is a pixel level fusion technique used to increase the spatial resolution of the multispectral (MS) image using spatial information from the high resolution (HR) panchromatic (Pan) image while preserving the spectral information in the MS image. This research proposes a new image super-resolution restoration algorithm. The development of the algorithm is based on the improvement of the classical projection onto convex set (POCS) algorithm and the stationary wavelet transform (SWT) to restore a super-resolution image from Egyptsat-1 low resolution (LR) images. Egyptsat-1 bands have inconsistent sub-pixel shift. This inconsistent shift between the bands can be changed into reliable shift by adaptive interpolation. Then, decomposition of high frequency sub-bands is generated using (SWT). The POCS iteration is used to restore high-resolution (HR) sub-bands from every LR images of the wavelet decomposition, and a HR image is reconstructed by inverse wavelet transform. Consequences show that the proposed methods yields significant spatial resolution improvements. The reconstructed image is evaluated by the Peak Signal to Noise Ratio (PSNR), Root Main Square Error (RMSE), Entropy, and Objective Fusion Measures. Different quantitative measures for the proposed method were assessed and tested with some implemented commonly used SR methods. The experimental results showed that this method can improve the ability of fusing different image information, and the visual and quantitative evaluations verify the usefulness and effectiveness of the proposed methodology. |