A unified probabilistic framework of robust and efficient color consistency correction for multiple images

Li, Yinxuan; Yin, Hongche; Yao, Jian; Wang, Hanyun; Li, Li

doi:10.1016/j.isprsjprs.2022.05.009

Cited by 10 publications

(4 citation statements)

References 31 publications

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“…Algorithms #5 and #6 are color-correction approaches for image sets (three or more images) and are based on global optimization methods. These are both often used as baseline methods for evaluating color correction approaches for image sets [ 30 , 31 , 54 , 58 , 66 , 67 ]. Algorithm #5 employs an optimization method to determine a global gain compensation to minimize the color difference in the overlapped regions of the images [ 39 ].…”

Section: Resultsmentioning

confidence: 99%

A Sequential Color Correction Approach for Texture Mapping of 3D Meshes

Dal’Col

Coelho

Madeira

et al. 2023

Sensors

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Texture mapping can be defined as the colorization of a 3D mesh using one or multiple images. In the case of multiple images, this process often results in textured meshes with unappealing visual artifacts, known as texture seams, caused by the lack of color similarity between the images. The main goal of this work is to create textured meshes free of texture seams by color correcting all the images used. We propose a novel color-correction approach, called sequential pairwise color correction, capable of color correcting multiple images from the same scene, using a pairwise-based method. This approach consists of sequentially color correcting each image of the set with respect to a reference image, following color-correction paths computed from a weighted graph. The color-correction algorithm is integrated with a texture-mapping pipeline that receives uncorrected images, a 3D mesh, and point clouds as inputs, producing color-corrected images and a textured mesh as outputs. Results show that the proposed approach outperforms several state-of-the-art color-correction algorithms, both in qualitative and quantitative evaluations. The approach eliminates most texture seams, significantly increasing the visual quality of the textured meshes.

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Section: Resultsmentioning

confidence: 99%

A Sequential Color Correction Approach for Texture Mapping of 3D Meshes

Dal’Col

Coelho

Madeira

et al. 2023

Sensors

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“…Выбор метрики для оценки качества выполнения цветовой коррекции является достаточно сложной задачей [12,25]. В современных работах используются как классические метрики, такие как пиковое отношение сигнал/шум (PSNR) и расстояние в равноконтрастном пространстве CIE La*b* [12,31], так и более новые, такие как метрика структурной схожести (SSIM) или метрики, основанные на анализе локальных распределений цветовых гистограмм [25].…”

Section: критерий качестваunclassified

“…Наряду с прогрессом в задачах повышения качества изображений [9], одной из важнейших задач при обработке цвета изображений является задача обеспечения похожести или консистентности цветов на изображениях, полученных с различных камер [10,11]. Проблема обеспечения схожести цветов при различных способах регистрации или синтеза изображения важна как при обработке фото-и видеоданных с различных источников [5,11], так и при анализе данных ДЗЗ [12,13] и синтезе виртуальной реальности [5].…”

Section: Introductionunclassified

Color consistency method for cameras with unknown model

Bibikov,

Petrov,

Alekseyev

et al. 2023

Computer Optics

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Modern methods of computational photography make it possible to bring the quality of images obtained by mobile cameras closer to the quality of professional cameras. One of the most important tasks is that of ensuring the consistency of colors from different cameras. In this paper, we propose a simple and efficient way to bring the colors of one camera to another, based on the approximation of the required transformation by a tone correction spline and a color transformation matrix. An experimental study was carried out in a rather complicated case, in which it was required to match colors of the images obtained from two fundamentally different sensors, as well as using diffractive optics. The results of the experiments showed that the proposed method allows one to obtain a higher accuracy of color matching between cameras than existing analogues.

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“…The first problem is that there are large color differences between adjacent orthoimages due to different illumination and exposure settings. This problem can be solved using a color correction [8][9][10][11][12][13] or image blending approach [14,15]. The second problem is that geometric misalignments exist between adjacent images, especially for orthoimages.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Local Alignment along the Seamline for Parallax-Tolerant Orthoimage Mosaicking

Yin

Li²,

Shi³

et al. 2022

Remote Sensing

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Orthoimage mosaicking with obvious parallax caused by geometric misalignment is a challenging problem in the field of remote sensing. Because the obvious objects are not included in the digital terrain model (DTM), large parallax exists in these objects. A common strategy is to search an optimal seamline between orthoimages, avoiding the majority of obvious objects. However, stitching artifacts may remain because (1) the seamline may still cross several obvious objects and (2) the orthoimages may not be precisely aligned in geometry when the accuracy of the DTM is low. While applying general image warping methods to orthoimages can improve the local geometric consistency of adjacent images, these methods usually significantly modify the geometric properties of orthophoto maps. To the best of our knowledge, no approach has been proposed in the field of remote sensing to solve the problem of local geometric misalignments after orthoimage mosaicking with obvious parallax. In this paper, we creatively propose a method to optimize local alignment along the seamline after seamline detection. It consists of the following main processes. First, we locate regions with geometric misalignments along the seamline based on the similarity measure. Second, for any one region, we find one-dimensional (1D) feature matches along the seamline using a semi-global matching approach. The deformation vectors are calculated for these matches. Third, these deformation vectors are robustly and smoothly propagated into the buffer region centered on the seamline by minimizing the associated energy function. Finally, we directly warp the orthoimages to eliminate the local parallax under the guidance of dense deformation vectors. The experimental results on several groups of orthoimages show that our proposed approach is capable of eliminating the local parallax existing in the seamline while preserving most geometric properties of digital orthophoto maps, and that it outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.

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A unified probabilistic framework of robust and efficient color consistency correction for multiple images

Cited by 10 publications

References 31 publications

A Sequential Color Correction Approach for Texture Mapping of 3D Meshes

A Sequential Color Correction Approach for Texture Mapping of 3D Meshes

Color consistency method for cameras with unknown model

Optimizing Local Alignment along the Seamline for Parallax-Tolerant Orthoimage Mosaicking

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