{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/color-image-enhancement-using-the-lrgb","title":"Color Image Enhancement Using the lrgb Coordinates in the Context of Support Fuzzification","arxiv_id":"1502.04499","date":"2015-02-16","proceeding":null,"authors":["Vasile Patrascu"],"abstract":"Image enhancement is an important stage in the image-processing domain. The\nmost known image enhancement method is the histogram equalization. This method\nis an automated one, and realizes a simultaneous modification for brightness\nand contrast in the case of monochrome images and for brightness, contrast,\nsaturation and hue in the case of color images. Simple and efficient methods\ncan be obtained if affine transforms within logarithmic models are used. A very\nimportant thing in the affine transform determination for color images is the\ncoordinate system that is used for color space representation. Thus, the using\nof the RGB coordinates leads to a simultaneous modification of luminosity and\nsaturation. In this paper using the lrgb perceptual coordinates one can define\naffine transforms, which allow a separated modification of luminosity l and\nsaturation s (saturation being calculated with the component rgb in the\nchromatic plane). Better results can be obtained if partitions are defined on\nthe image support and then the pixels are separately processed in each window\nbelonging to the defined partition. Classical partitions frequently lead to the\nappearance of some discontinuities at the boundaries between these windows. In\norder to avoid all these drawbacks the classical partitions may be replaced by\nfuzzy partitions. Their elements will be fuzzy windows and in each of them\nthere will be defined an affine transform induced by parameters using the fuzzy\nmean, fuzzy variance and fuzzy saturation computed for the pixels that belong\nto the analyzed window. The final image is obtained by summing up in a weight\nway the images of every fuzzy window.","url_abs":"http://arxiv.org/abs/1502.04499v1","url_pdf":"http://arxiv.org/pdf/1502.04499v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"color-image-enhancement-using-the-lrgb","repo_url":"https://github.com/BushrHaddad/Color-Image-Enhancement-Using-the-Support-Fuzzification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-enhancement","task_name":"Image Enhancement"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}