Pansharpening is an effective way to enhance the spatial resolution of a multispectral (MS) image by fusing it with a provided panchromatic image. Instead of restricting the coding coefficients of low-resolution (LR) and high-resolution (HR) images to be equal, we propose a pansharpening approach via sparse regression in which the relationship between sparse coefficients of HR and LR MS images is modeled by ridge regression and elastic-net regression simultaneously learning the corresponding dictionaries. The compact dictionaries are learned based on the sampled patch pairs from the high- and low-resolution images, which can greatly characterize the structural information of the LR MS and HR MS images. Later, taking the complex relationship between the coding coefficients of LR MS and HR MS images into account, the ridge regression is used to characterize the relationship of intrapatches. The elastic-net regression is employed to describe the relationship of interpatches. Thus, the HR MS image can be almost identically reconstructed by multiplying the HR dictionary and the calculated sparse coefficient vector with the learned regression relationship. The simulated and real experimental results illustrate that the proposed method outperforms several well-known methods, both quantitatively and perceptually.
A single sensor camera can capture scenes by means of a color filter array. Each pixel samples only one of the three primary colors. We use a color demosaicking (CDM) technique to produce full color images and propose a robust adaptive sparse representation model for high quality CDM. The data fidelity term is characterized by l1 norm to suppress the heavy-tailed visual artifacts with an adaptively learned dictionary, while the regularization term is encouraged to seek sparsity by forcing sparse coding close to its nonlocal means to reduce coding errors. Based on the classical quadratic penalty function technique in optimization and an operator splitting method in convex analysis, we further present an effective iterative algorithm to solve the variational problem. The efficiency of the proposed method is demonstrated by experimental results with simulated and real camera data.
Most existing superresolution (SR) techniques focus primarily on improving the quality in the luminance component of SR images, while paying less attention to the chrominance component. We present an edge and color preserving image SR approach. First, for the luminance channel, a heavy-tailed gradient distribution of natural images is investigated as an image prior. Then, an efficient optimization algorithm is developed to recover the latent high-resolution (HR) luminance component. Second, for the chrominance channels, we propose a two-stage framework for luminance-guided chrominance SR. In the first stage, since most of the shape and structural information is contained in the luminance channel, a simple Markov random field formulation is introduced to search the optimal direction for color local interpolation guided by HR luminance components. To further improve the quality of the chrominance channels, in the second stage, a nonlocal auto regression model is utilized to refine the initial HR chrominance. Finally, we combine the SR reconstructed luminance components with the generated HR chrominance maps to get the final SR color image. Systematic experimental results demonstrated that our method outperforms some state-of-the-art methods in terms of the peak signal-to-noise ratio, structural similarity, feature similarity, and the mean color errors.
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