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                    "codemeta:author": {
                        "codemeta:affiliation": "Universitat Illes Balears, Spain",
                        "codemeta:name": "Jose-Luis Lisani"
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                    "codemeta:dateCreated": "2018-11-14",
                    "codemeta:datePublished": "2018-12-07",
                    "codemeta:description": "Implementation of Shape Preserving Local Histogram Modification Algorithm",
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                        "codemeta:identifier": "ISSN: 2105-1232 DOI: 10.5201/ipol",
                        "codemeta:name": "Image Processing On Line (IPOL)",
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                    },
                    "codemeta:keywords": [
                        "contrast enhancement",
                        "histogram modification",
                        "local processing"
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                        "codemeta:abstract": "In this paper we describe the implementation of the algorithm for local contrast enhancement published by Caselles et al. in 1999. This algorithm was the first designed explicitly to increase the contrast while preserving the so-called 'shape structure' of the image, that is, its set of level sets. According to the mathematical morphology school, artifacts are created when this structure is modified. The original algorithm is described and also two alternative implementations are proposed, which limit the over-enhancement of noise.",
                        "codemeta:identifier": "https://doi.org/10.5201/ipol.2018.236",
                        "codemeta:name": "An Analysis and Implementation of the Shape Preserving\n                Local Histogram Modification Algorithm",
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                    },
                    "codemeta:relatedLink": "http://ipolcore.ipol.im/demo/clientApp/demo.html?id=236",
                    "codemeta:releaseNotes": "Given an input image this demo computes the result of the three versions of the MLHE algorithm described in the associated article: MLHE HE, MLHE PAE and MLHE CLAHE. The users can select the parameters of each method. The default parameters are the same described in the article. For comparison, the results of classical histogram equalization (HE) and CLAHE are also displayed. We use obtained the Matlab implementation of the CLAHE algorithm. Both HE and CLAHE are applied on the intensity component of the input image and the output color image is obtained as in the MLHE methods.",
                    "codemeta:url": "https://www.ipol.im/pub/art/2018/236/",
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                    "title": "mlheIPOL"
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