Color constancy based on local space average color

Ebner, Marc

doi:10.1007/s00138-008-0126-2

Cited by 81 publications

(53 citation statements)

References 56 publications

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“…where A is the atmospheric light, which can be estimated by the dehazing algorithm, and we apply that of Kim et al [29] A typical value of thredWB is 50, and then, we use the gray world [34] for white balancing. The gray world assumes that the brightest pixel of the grayscale image is white on its color image.…”

Section: Flowchart Of the Proposed Methodsmentioning

confidence: 99%

“…Finally we stretch the image by Equation (6), which needs a few matrix operations. For white balancing, we use the gray world [34], which is also an O(n) time algorithm. Furthermore, in the step of image refinement, due to the spatial smoothness of the estimated components U and D, it is possible to introduce the up-/down-sampling technique to reduce the input scale of the algorithm to save processing time.…”

Section: Flowchart Of the Proposed Methodsmentioning

confidence: 99%

“…However, there is no general restoration model for such degraded images. Research mainly applies the white balance [32][33][34] to correct the color bias at first and then uses a series of contrast stretch processing to enhance the visibility of underwater images.…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning

Han

2017

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Images captured in bad conditions often suffer from low contrast. In this paper, we proposed a simple, but efficient linear restoration model to enhance the low contrast images. The model's design is based on the effective space of the 3D surface graph of the image. Effective space is defined as the minimum space containing the 3D surface graph of the image, and the proportion of the pixel value in the effective space is considered to reflect the details of images. The bright channel prior and the dark channel prior are used to estimate the effective space, however, they may cause block artifacts. We designed the pixel learning to solve this problem. Pixel learning takes the input image as the training example and the low frequency component of input as the label to learn (pixel by pixel) based on the look-up table model. The proposed method is very fast and can restore a high-quality image with fine details. The experimental results on a variety of images captured in bad conditions, such as nonuniform light, night, hazy and underwater, demonstrate the effectiveness and efficiency of the proposed method.

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Section: Flowchart Of the Proposed Methodsmentioning

confidence: 99%

Section: Flowchart Of the Proposed Methodsmentioning

confidence: 99%

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Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning

Han

2017

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“…A sophisticated and computationally intensive approach, like Local Space Average Color [3], is unlikely to be adopted by the object recognition community as a pre-processing step. We briefly review the methods we evaluate since some of them are not well known in the computer vision community.…”

Section: Methodsmentioning

confidence: 99%

“…Qualitatively, this method only works well if there are a sufficiently large number of different colors exhibited in a scene [3]. Despite this, it may still be beneficial for discrimination.…”

Section: Grey Worldmentioning

confidence: 99%

Color Constancy Algorithms for Object and Face Recognition

Kanan

Flores

Cottrell

2010

Advances in Visual Computing

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Abstract. Brightness and color constancy is a fundamental problem faced in computer vision and by our own visual system. We easily recognize objects despite changes in illumination, but without a mechanism to cope with this, many object and face recognition systems perform poorly. In this paper we compare approaches in computer vision and computational neuroscience for inducing brightness and color constancy based on their ability to improve recognition. We analyze the relative performance of the algorithms on the AR face and ALOI datasets using both a SIFT-based recognition system and a simple pixel-based approach. Quantitative results demonstrate that color constancy methods can significantly improve classification accuracy. We also evaluate the approaches on the Caltech-101 dataset to determine how these algorithms affect performance under relatively normal illumination conditions.

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Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma

Keel

Lee

et al. 2019

JAMA Ophthalmol

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The purpose of this study was to develop a 3D deep learning system from spectral domain optical coherence tomography (SD-OCT) macular cubes to differentiate between referable and nonreferable cases for glaucoma applied to real-world datasets to understand how this would affect the performance.Methods: There were 2805 Cirrus optical coherence tomography (OCT) macula volumes (Macula protocol 512 × 128) of 1095 eyes from 586 patients at a single site that were used to train a fully 3D convolutional neural network (CNN). Referable glaucoma included true glaucoma, pre-perimetric glaucoma, and high-risk suspects, based on qualitative fundus photographs, visual fields, OCT reports, and clinical examinations, including intraocular pressure (IOP) and treatment history as the binary (two class) ground truth. The curated real-world dataset did not include eyes with retinal disease or nonglaucomatous optic neuropathies. The cubes were first homogenized using layer segmentation with the Orion Software (Voxeleron) to achieve standardization. The algorithm was tested on two separate external validation sets from different glaucoma studies, comprised of Cirrus macular cube scans of 505 and 336 eyes, respectively. Results:The area under the receiver operating characteristic (AUROC) curve for the development dataset for distinguishing referable glaucoma was 0.88 for our CNN using homogenization, 0.82 without homogenization, and 0.81 for a CNN architecture from the existing literature. For the external validation datasets, which had different glaucoma definitions, the AUCs were 0.78 and 0.95, respectively. The performance of the model across myopia severity distribution has been assessed in the dataset from the United States and was found to have an AUC of 0.85, 0.92, and 0.95 in the severe, moderate, and mild myopia, respectively.Conclusions: A 3D deep learning algorithm trained on macular OCT volumes without retinal disease to detect referable glaucoma performs better with retinal segmentation preprocessing and performs reasonably well across all levels of myopia.Translational Relevance: Interpretation of OCT macula volumes based on normative data color distributions is highly influenced by population demographics and characteristics, such as refractive error, as well as the size of the normative database. Referable glaucoma, in this study, was chosen to include cases that should be seen by a specialist. This study is unique because it uses multimodal patient data for the glaucoma definition, and includes all severities of myopia as well as validates the algorithm with international data to understand generalizability potential.

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Color constancy based on local space average color

Cited by 81 publications

References 56 publications

Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning

Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning

Color Constancy Algorithms for Object and Face Recognition

Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma

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