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Single-image super-resolution (SR) reconstruction via sparse representation has recently attracted broad interest. It is known that a low-resolution (LR) image is susceptible to noise or blur due to the degradation of the observed image, which would lead to a poor SR performance. In this paper, we propose a novel robust edge-preserving smoothing SR (REPS-SR) method in the framework of sparse representation. An EPS regularization term is designed based on gradient-domain-guided filtering to preserve image edges and reduce noise in the reconstructed image. Furthermore, a smoothing-aware factor adaptively determined by the estimation of the noise level of LR images without manual interference is presented to obtain an optimal balance between the data fidelity term and the proposed EPS regularization term. An iterative shrinkage algorithm is used to obtain the SR image results for LR images. The proposed adaptive smoothing-aware scheme makes our method robust to different levels of noise. Experimental results indicate that the proposed method can preserve image edges and reduce noise and outperforms the current state-of-the-art methods for noisy images.

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Pan-sharpening aims to sharpen a low spatial resolution multispectral (MS) image by combining the spatial detail information extracted from a panchromatic (PAN) image. An effective pan-sharpening method should produce a high spatial resolution MS image while preserving more spectral information. Unlike traditional intensity-hue-saturation (IHS)-and principal component analysis (PCA)-based multiscale transform methods, a novel pan-sharpening framework based on the matting model (MM) and multiscale transform is presented in this paper. First, we use the intensity component (I) of the MS image as the alpha channel to generate the spectral foreground and background. Then, an appropriate multiscale transform is utilized to fuse the PAN image and the upsampled I component to obtain the fused high-resolution gray image. In the fusion, two preeminent fusion rules are proposed to fuse the low-and high-frequency coefficients in the transform domain. Finally, the high-resolution sharpened MS image is obtained by linearly compositing the fused gray image with the upsampled foreground and background images. The proposed framework is the first work in the pan-sharpening field. A large number of experiments were tested on various satellite datasets; the subjective visual and objective evaluation results indicate that the proposed method performs better than the IHS-and PCA-based frameworks, as well as other state-of-the-art pan-sharpening methods both in terms of spatial quality and spectral maintenance.

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