Hue-correction scheme considering CIEDE2000 for color-image enhancement including deep-learning-based algorithms

Kinoshita, Yuma; Kiya, Hitoshi

doi:10.1017/atsip.2020.17

Cited by 10 publications

(5 citation statements)

References 35 publications

(87 reference statements)

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“…Colorimetry relates human subjective color perception to objective physical measurements, which uses the Lab (luminance, red/green difference, yellow/blue difference) color space. The CIEDE2000 is regarded as the best mutual matching chromatic aberration formula with human vision in theory currently [46][47][48][49]. Lab and CIEDE2000 are employed in this paper to extract and compare the plumb-line neighborhood primary colors.…”

Section: Calculation Of Chromatic Aberrationmentioning

confidence: 99%

The Plumb-Line Matching Algorithm for UAV Oblique Photographic Photos

Zhang,

Sun,

Gao

et al. 2023

Remote Sensing

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Building facades has always been a challenge for feature matching in oblique photogrammetry due to weak textures, non-Lambertian objects, severe occlusion, and distortion. Plumb lines are essential building geometry structural feature lines in building facades, which show strong spatial relevance to these problems. Achieving plumb line matching has great application potential for optimizing the process and products of oblique photogrammetry. Thus, we proposed a novel matching algorithm for plumb lines based on spatial and color hybrid constraints according to its central projection imaging characteristics. Firstly, based on vanishing point theory, the plumb lines from photos were back-calculated to determine the matching target set; secondly, the property of its large elevation ranges was exploited to calculate the homonymous points as spatial constraints by projecting plumb lines onto the stratified spatial planes; thirdly, the neighboring primary colors on both sides of the plumb lines were extracted as feature descriptors and compared by colorimetry; then, the greedy strategy was employed to successively filter out the locally optimal solutions satisfying the spatial and color hybrid constraints to complete the initial matching; finally, the intersection-over-union analysis of the solution plane and the verticalness evaluation of the matching results were implemented to eliminate errors. The results show that the proposed algorithm can achieve an average accuracy of 97.29% and 78.41% in the forward and lateral overlap experiments from multi-scenes, respectively, displaying a strong adaptability to poor texture, inconsistency, and distortion. In conclusion, thanks to the plumb-line-oriented matching strategy, this algorithm owns inherent advantages in theory and computational complexity. It is suitable for all building-oriented oblique photogrammetry tasks and is highly worthy of promotion and application.

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Section: Calculation Of Chromatic Aberrationmentioning

confidence: 99%

The Plumb-Line Matching Algorithm for UAV Oblique Photographic Photos

Zhang,

Sun,

Gao

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…A) Analysis of color spaces [25] explained the issue where the results of colorization using the Lab color space, despite being extensively utilized for modeling human perception such as [38], are tend to be low saturation. We believe that the definition of ab channels in Lab makes it hard to represent the saturation in a straightforward way because two parameters, a and b, are both involved with the saturation nonlinearly.…”

Section: I P R O P O S E D M E T H O Dmentioning

confidence: 99%

Two-stage pyramidal convolutional neural networks for image colorization

Wei

Kuo

et al. 2021

SIP

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The development of colorization algorithms through deep learning has become the current research trend. These algorithms colorize grayscale images automatically and quickly, but the colors produced are usually subdued and have low saturation. This research addresses this issue of existing algorithms by presenting a two-stage convolutional neural network (CNN) structure with the first and second stages being a chroma map generation network and a refinement network, respectively. To begin, we convert the color space of an image from RGB to HSV to predict its low-resolution chroma components and therefore reduce the computational complexity. Following that, the first-stage output is zoomed in and its detail is enhanced with a pyramidal CNN, resulting in a colorized image. Experiments show that, while using fewer parameters, our methodology produces results with more realistic color and higher saturation than existing methods.

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“…In contrast, since cameras do not intrinsically have this ability, white balancing is applied to images [ 5 ]. Otherwise, in the field of image segmentation or object recognition, we may suffer from color distortion caused by lighting effects [ 2 , 3 , 6 , 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Three-Color Balancing for Color Constancy Correction

et al. 2021

Self Cite

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This paper presents a three-color balance adjustment for color constancy correction. White balancing is a typical adjustment for color constancy in an image, but there are still lighting effects on colors other than white. Cheng et al. proposed multi-color balancing to improve the performance of white balancing by mapping multiple target colors into corresponding ground truth colors. However, there are still three problems that have not been discussed: choosing the number of target colors, selecting target colors, and minimizing error which causes computational complexity to increase. In this paper, we first discuss the number of target colors for multi-color balancing. From our observation, when the number of target colors is greater than or equal to three, the best performance of multi-color balancing in each number of target colors is almost the same regardless of the number of target colors, and it is superior to that of white balancing. Moreover, if the number of target colors is three, multi-color balancing can be performed without any error minimization. Accordingly, we propose three-color balancing. In addition, the combination of three target colors is discussed to achieve color constancy correction. In an experiment, the proposed method not only outperforms white balancing but also has almost the same performance as Cheng’s method with 24 target colors.

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Hue-correction scheme considering CIEDE2000 for color-image enhancement including deep-learning-based algorithms

Cited by 10 publications

References 35 publications

The Plumb-Line Matching Algorithm for UAV Oblique Photographic Photos

The Plumb-Line Matching Algorithm for UAV Oblique Photographic Photos

Two-stage pyramidal convolutional neural networks for image colorization

Three-Color Balancing for Color Constancy Correction

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