A ModelBased Method for PanSharpening of MultiSpectral Images using Sparse Representation Pansharpening (PS) is fusion of the lowresolution multispectral (LRM) image with the corresponding high resolution panchromatic (HRP) one, which aims to reach the highresolution multispectral (HRM) image. Due to the importance of designing welladapted dictionaries for the pansharpening problem as the fundamental challenge in the pansharpening methods utilizing sparse representation, we present a novel strategy to tackle this issue. Our method takes the image formation model into account in a patchbased strategy and exploits local spectralspatial information. To make a fair tradeoff between spectral and spatial information of each patch, some local parameters are considered and estimated adaptively. In addition, the energy ratio between LRMLRP patches is considered to locally reconstruct the same ratio for the corresponding HRMHRP patches. This modelbased pansharpening strategy led to a closedform solution, which results in precise HRM dictionary atoms. To obtain the unknown HRM image, after designing the HRM dictionary from input Pan and multispectral images, sparse representation over LRM and HRM dictionaries will be conducted in the sparse coding stage. The proposed method has been applied to two different datasets collected by WorldView3 and GeoEye1, and then compared with some popular and stateoftheart methods, qualitatively and quantitatively.

Superresolution with Nonlocal Regularized Sparse Representation The reconstruction of a high resolution (HR) image from its low resolution (LR) counterpart is a challenging problem. The recently developed sparse representation (SR) techniques provide new solutions to this inverse problem by introducing the l 1 norm sparsity prior into the superresolution reconstruction process. In this paper, we present a new SR based image superresolution by optimizing the objective function under an adaptive sparse domain and with the nonlocal regularization of the HR images. The adaptive sparse domain is estimated by applying principal component analysis to the grouped nonlocal similar image patches. The proposed objective function with nonlocal regularization can be efficiently solved by an iterative shrinkage algorithm. The experiments on natural images show that the proposed method can reconstruct HR images with sharp edges from degraded LR images.