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Dark Image Enhancement Using Perceptual Color Transfer IEEE Access, vol. 5, 2017, pp. 1-1. Dark image enhancement using perceptual color transfer. Cepeda-Negrete, J., Sanchez-Yanez, RE., Correa-Tome, Fernando E y Lizarraga-Morales, Rocio A. Cita: Cepeda-Negrete, J., Sanchez-Yanez, RE., Correa-Tome, Fernando E y Lizarraga-Morales, Rocio A (2017). Dark image enhancement using perceptual color transfer. IEEE Access, 5 1-1. Dirección estable: https://www.aacademica.org/jcepedanegrete/14 Esta obra está bajo una licencia de Creative Commons. Para ver una copia de esta licencia, visite http://creativecommons.org/licenses/by-nc-nd/4.0/deed.es. Acta Académica es un proyecto académico sin fines de lucro enmarcado en la iniciativa de acceso abierto. Acta Académica fue creado para facilitar a investigadores de todo el mundo el compartir su producción académica. Para crear un perfil gratuitamente o acceder a otros trabajos visite: http://www.aacademica.org. Received September 21, 2017, accepted October 13, 2017. Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. Digital Object Identifier 10.1109/ACCESS.2017.2763898 Dark Image Enhancement Using Perceptual Color Transfer JONATHAN CEPEDA-NEGRETE 1, RAUL E. SANCHEZ-YANEZ 2, (Member, IEEE), FERNANDO E. CORREA-TOME2, AND ROCIO A. LIZARRAGA-MORALES3, (Member, IEEE) AQ:1 1Department of Agricultural Engineering, University of Guanajuato DICIVA, Guanajuato 36500 , Mexico 2Department of Electronics Engineering, University of Guanajuato DICIS, Guanajuato 36500, Mexico 3Department of Multidisciplinary Studies, University of Guanajuato DICIS, Guanajuato 36500, Mexico Corresponding author: Raul E. Sanchez-Yanez ([email protected]) The work of J. Cepeda-Negrete was supported by the Mexican National Council on Science and Technology (CONACyT) through the Scholarship 290747 under Grant 388681/254884. 1 ABSTRACT In this paper, we introduce an image enhancing approach for transforming dark images into 2 lightened scenes, and we evaluate such method in different perceptual color spaces, in order to find the 3 best-suited for this particular task. Specifically, we use a classical color transfer method where we obtain 4 first-order statistics from a target image and transfer them to a dark input, modifying its hue and brightness. 5 Two aspects are particular to this paper, the application of color transfer on dark imagery and in the search 6 for the best color space for the application. In this regard, the tests performed show an accurate transference 7 of colors when using perceptual color spaces, being RLAB the best color space for the procedure. Our 8 results show that the methodology presented in this paper can be a good alternative to low-light or night 9 vision processing techniques. Besides, the proposed method has a low computational complexity, property 10 that is important for real time applications or for low-resource systems. This method can be used as a 11 preprocessing step in order to improve the recognition and interpretation of dark imagery in a wide range of 12 applications. 13 INDEX TERMS Perceptual color space, color transfer, dark image enhancement, night vision. 14 I. INTRODUCTION representation may facilitate the recognition of night vision 34 15 Image enhancement is a challenging task in the image pro- imagery and its interpretation [8]. 35 16 cessing field. The objective of image enhancement algorithms Most of classical methods that have been used over decades 36 17 is to improve the quality and to ease the visibility of a given for dark image enhancement are histogram-based; for exam- 37 18 image, e.g. an image with noise, an image with low con- ple, histogram stretching, histogram equalization, brightness 38 19 trast, or a dark image. With such improvement, images are preserving bi-histogram equalization [11], contrast limited 39 20 more useful in application fields like human understanding, adaptive histogram equalization [12], [13], to mention a 40 21 digital photography [1], medical image analysis [2], object few. The aforementioned methods have been categorized as 41 22 detection [3], face recognition [4], and video surveillance [5]. direct and indirect methods [14]. Direct methods consist of 42 23 Dark images, or images taken under low light conditions, improving image contrast by optimizing an objective contrast 43 24 are problematic because of their narrow dynamic range. measure. Indirect methods exploit the dynamic range without 44 25 Under these conditions, a regularIEEE camera sensor introduces usingProof a contrast measure. However, the performance of such 45 26 significant amounts of noise, further reducing the information histogram-based algorithms is very limited with color images 46 27 content in the image. Because of these limitations, dark image because these methods change the correlation between the 47 28 enhancement algorithms occasionally produce artifacts in color components of the original scene. Most recently, a num- 48 29 the processed images [6]. One traditional approach to dark ber of methods for improving the contrast using a global 49 30 image enhancement is to use monochromatic representations mapping from feature analysis have been published specially 50 31 and ignore the color features. However, color images have oriented to video enhancement [15], [16]. 51 32 numerous benefits over monochromatic images for surveil- Color constancy approaches are also used to increase the 52 33 lance and security applications [7]–[10]. Moreover, a color overall luminance in the image. Although color constancy 53 2169-3536 2017 IEEE. Translations and content mining are permitted for academic research only. VOLUME 5, 2017 Personal use is also permitted, but republication/redistribution requires IEEE permission. 1 See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. J. Cepeda-Negrete et al.: Dark Image Enhancement Using Perceptual Color Transfer 54 algorithms have been originally developed to estimate the In this work, we propose the applicability of a well- 110 55 color of a light source by discarding the illuminant from the known statistical method for color transfer [28], now using 111 56 scene, they also improve the chromatic content [17]. Some the color mapping to transform dark images into daylight- 112 57 works have explored the use of color constancy algorithms for looking images. Few previous works already have studied 113 58 color image enhancement purposes [18], [19]. In particular, night-time imagery enhancement. However, images used in 114 59 [18] was oriented to local contrast enhancement using the those studies are professional landscapes with a controlled 115 60 White-Patch and the Gray-World algorithms in combination exposure [39], [40], or they were created by the fusion of 116 61 with an automatic color equalization technique. In this work, images from different spectral or color bands [8], [41]–[43]. 117 62 and just for comparison purposes, we have included the two Our approach uses only a single image which is completely 118 63 aforementioned color constancy algorithms to enhance dark dark (without adjusted exposure), and obtained from a com- 119 64 images. mon RGB-CCD sensor. Furthermore, most of the color trans- 120 65 Image fusion is another approach used to enhance dark fer research has been performed using the lαβ color space; 121 66 images. This technique increases the visual information in few studies have been focused on other perceptual spaces and 122 67 an image by combining different bands or images into the none on RLAB space. In this study, we propose to apply the 123 68 RGB space [9], [10], [20]. In image fusion, generally two color transfer using RGB and four perceptual color spaces: 124 69 monochromatic images from different spectral bands are lαβ, CIELUV, CIELAB and RLAB. The latter color space, 125 70 used. A near-infrared or a visible image is considered as designed specifically to emulate the human perception of 126 71 the R component, and a thermal image is designated as color under extreme light conditions [44]. Dark or night 127 72 the G component [8], [21]. This combination of bands is imagery can be included in such kind of conditions. The per- 128 73 used to build a look-up table (LUT) to transfer colors to formance of the color transfer using all color spaces is com- 129 74 other images. However, this scheme may produce images pared. Additionally, we include in the comparison the results 130 75 with false colors (i.e. colors that are not actually in the yielded by image enhancement methods used as reference. 131 76 scene). These false colors could also diminish the scene Tests are conducted in order to support our hypothesis: better 132 77 comprehension [22], [23]. results are obtained when the color transfer is performed on 133 78 Color transfer (a.k.a. color mapping) is an efficient the RLAB space. 134 79 approach to enhance an image under low light conditions The rest of this article is organized as follows. The pro- 135 80 avoiding false colors. This technique recolors an input image posed framework is presented in Section II, including the 136 81 by transferring the color content from another image used description of the color spaces considered, the formula- 137 82 as a reference (target). There are three main strategies used tion of the color transfer function, and the method used 138 83 for color transfer between images: geometric-based methods, to asses the correct color transfer. Experimental results are 139 84 user-aided solutions, and statistical approaches. In geometric- discussed in Section III, followed by the concluding remarks 140 85 based methods [24], the transfer of aspects of color rendition in Section IV. 141 86 from one image to another can be facilitated by searching 87 for corresponding features that are depicted in both images. II. METHODOLOGY 142 88 By actively finding correspondences between pairs of images, This section introduces the perceptual color transfer (PCT) 143 89 the color transfer algorithm can better ensure that features that technique used in this work to transform a dark image into a 144 90 occur in both images end up having the same colors.
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