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Converting a color image into a grayscale image is a complex problem that is based on preserving color contrast, sharpness, and luminance. In this paper, a novel image decolorization algorithm is proposed using High Dimensional Model Representation (HDMR) with an optimization procedure that retains color content and contrast. In the proposed algorithm, a color image is first decomposed into HDMR components and then the components are categorized depending on whether they are colored or colorless. After that, the image is reconstructed by merging the weighted colored and colorless HDMR components. The weight coefficients are determined by an optimization process. The proposed algorithm both visually and quantitatively compared with state-of-the-art methods in the literature using various performance evaluation metrics. As regards all obtained results, the HDMR based image decolorization algorithm is more potent and has better performance in overall comparison. Most importantly, this algorithm has a flexible structure as it is able to produce various grayscale images for different thresholds of visible color contrast which makes this algorithm superior given that it is the only one that accomplishes this feat in the literature to the best of our knowledge. [Display omitted] • Focussing on a novel HDMR-based color-to-gray image conversion. • Observation of HDMR components with different properties. • The unique usage of HDMR method in decolorization. • User-driven decolorization algorithm. • Flexibility of selecting grayscale image for different threshold values. [ABSTRACT FROM AUTHOR]

• A novel pansharpening method based on Adaptive HDMR is proposed. • Proposed algorithm is a component substitution-based method and is easy to implement. • Proposed algorithm provides more spectral accuracy than traditional CS-based methods. • Proposed algorithm outperforms state-of-the-art image pansharpening methods. Pansharpening methods are used to enhance the spatial resolution of a low resolutional multispectral (MS) image by fusing with a high resolutional panchromatic image (PAN). The main difficulty of pansharpening is avoiding spectral distortion while getting a sharpened MS image with high spatial resolution. Intensity-Hue-Saturation (IHS) based methods are applied to transform from color space to IHS and provide equalization of a PAN component with an MS image to eliminate distortion problems. However, most of the modified IHS methods still cause spectral distortion. To overcome this problem, a novel pansharpening method, based on Adaptive High Dimensional Model Representation is proposed in this article. HDMR is a well-known decomposition method for multivariate functions and data sets. The algorithm we propose includes three stages: the first stage is to obtain HDMR components of the MS image using the HDMR decomposition and then to use scaling factors to optimize the effects of the information the components hold. The second stage requires the calculation of some weighting factors in each band to minimize the spectral distortion. Computing the spatial details obtained from the difference between the PAN image and the Adaptive HDMR expansion of the MS image, and adding the difference to the MS image constitutes the third stage. Our proposed algorithm is easy to implement in pansharpening similar to component substitution (CS) based methods, HDMR terms are calculated once and then used adaptively by employing scaling and weighting factors which are determined through a straightforward methodology. The method also provides greater spectral fidelity than the traditional CS based methods as a result of the scaling factors. The proposed method has been tested on different MS images and compared with state-of-the-art pansharpening methods. The results are given both in terms of visual quality and numerical assessments. [ABSTRACT FROM AUTHOR]