Simplest Color Balance

Limare, Nicolas; Lisani, José Luis; Morel, Jean‐Michel; Petro, Ana Belén; Sbert, Catalina

doi:10.5201/ipol.2011.llmps-scb

Cited by 100 publications

(61 citation statements)

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“…We could apply other methods for image enhancement, previously to Retinex, to obtain better results. For example, to Figure 3 we can apply the "Simplest Color Balance" algorithm [8] and we can observe the better results (Figure 4). Figure 5 and Figure 6 demonstrate how Retinex can be used for removing shadows.…”

Section: Noisy Imagementioning

confidence: 99%

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Retinex Poisson Equation: a Model for Color Perception

Limare¹,

Petro²,

Sbert³

et al. 2011

Image Processing on Line

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In 1964 Edwin H. Land formulated the Retinex theory, the first attempt to simulate and explain how the human visual system perceives color. Unfortunately, the Retinex Land-McCann original algorithm is both complex and not fully specified. Indeed, this algorithm computes at each pixel an average of a very large set of paths on the image. For this reason, Retinex has received several interpretations and implementations which, among other aims, attempt to tune down its excessive complexity. But, Morel et al. have shown that the original Retinex algorithm can be formalized as a (discrete) partial differential equation. This article describes the PDE-Retinex, a fast implementation of the Land-McCann original theory using only two DFT's. Source CodeThe source code, the code documentation, and the online demo are accessible at the IPOL web part of this article 1 . In this link an implementation is available for download. Compilation and usage instruction are included in the README.txt file of the compressed archive.Keywords: PDE; Retinex; color OverviewIn 1964 Edwin H. Land [1] formulated the Retinex theory, the first attempt to simulate and explain how the human visual system perceives color. His theory and an extension, the "reset Retinex" [2] were further formalized by Land and McCann in 1971. Several Retinex algorithms have been developed ever since. These color constancy algorithms modify the RGB values at each pixel to give an estimate of the physical color independent of the shading.The Retinex original method was complex and imprecise. Indeed, this algorithm computes at each pixel an average of a very large and unspecified set of paths on the image. For this reason, Retinex has received several interpretations and implementations which, among other aims, attempt to tune down its excessive complexity.

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Section: Noisy Imagementioning

confidence: 99%

“…The paper of Bertalmio et al [7] is an example of a fast color enhancement algorithm. You can use the "Simplest Color Balance" algorithm [8] previously to Retinex, for improving the contrast of your image.…”

Section: Online Demomentioning

confidence: 99%

Retinex Poisson Equation: a Model for Color Perception

Limare¹,

Petro²,

Sbert³

et al. 2011

Image Processing on Line

Self Cite

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“…1차적으로 차량 후보 영역 검출을 위한 특징 추출로 차량 후면 전체 영역을 학습 한다 [1,8,9,10,11] . 따라서 차량을 세단, SUV [12] 를 통해 색을 보정하 고 후미등 하단 부를 이용하여 학습한 Haar-like 특징 [13] 을 사용하여 1차적으로 차량을 검출한다. 또한 차량과 비차량 간의 분류 시간을 줄이기 위해 차량이 존재할 수 있는 영역 에 한해 검증을 실시한다.…”

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“…Ⅲ. 자동차 영역 검출을 위한 전처리 [12] . 먼저 각 R, G, B 채널의 히스토그램을 0부터 255 사이의 범위로 스케일링한 후 입력 영상 픽셀 값들의 최소, 최대값을 구하기 위해 픽셀 값들을 정렬한다.…”

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Lower Tail Light Learning-based Forward Vehicle Detection System Irrelevant to the Vehicle Types

Ki¹,

Kwak²,

Byun³

2016

Journal of Broadcast Engineering

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Recently, there are active studies on a forward collision warning system to prevent the accidents and improve convenience of drivers. For collision evasion, the vehicle detection system is required. In general, existing learning-based vehicle detection methods use the entire appearance of the vehicles from rear-view images, so that each vehicle types should be learned separately since they have distinct rear-view appearance regarding the types. To overcome such shortcoming, we learn Haar-like features from the lower part of the vehicles which contain tail lights to detect vehicles leveraging the fact that the lower part is consistent regardless of vehicle types. As a verification procedure, we detect tail lights to distinguish actual vehicles and non-vehicles. If candidates are too small to detect the tail lights, we use HOG(Histogram Of Gradient) feature and SVM(Support Vector Machine) classifier to reduce false alarms. The proposed forward vehicle detection method shows accuracy of 95% even in the complicated images with many buildings by the road, regardless of vehicle types.

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Impact of fluorescent whitening agent excitation on White Balance Algorithms

Chen

Mao

2018

Color Research & Application

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Various white balance algorithms, based either on the Gray World assumption or on the Retinex theory, have been developed to remove the color cast of illumination in images. Though most man-made white objects contain different amount of fluorescent whitening agents (FWAs) to enhance and create different degrees of whiteness appearance, the FWA excitation has seldom been considered in white balance algorithms. In this study, four illuminants, comprising 2 levels of CCT and 2 levels of UV/violet radiations, were produced to illuminate a setup, which included a Macbeth ColorChecker and a calibrated whiteness standard. Three whiteness standards containing different amount of FWAs were used under each illuminant, with one present each time. It was found the FWA excitation generally caused little color difference to the algorithms based on the Gray World assumption, which was likely due to the small size of the whiteness standard in the image. In contrast, the performance of the algorithms based on the Retinex theory was largely affected by the FWA excitation, as the FWA excitation affected the RGB values of the pixels for adjusting the gain factors. Given the wide usage of blue-pumped white LEDs that contained little UV/ violet radiation for enhancing luminous efficacy in general illumination, the impact of the FWAs is not trivial and should be considered in developing white balance algorithms. K E Y W O R D S color difference, fluorescent whitening agents (FWAs), LED, image quality, White balance algorithms Color Res Appl. 2018;1-12. wileyonlinelibrary.com/journal/col V C 2018 Wiley Periodicals, Inc. | 1

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Simplest Color Balance

Cited by 100 publications

References 1 publication

Retinex Poisson Equation: a Model for Color Perception

Retinex Poisson Equation: a Model for Color Perception

Lower Tail Light Learning-based Forward Vehicle Detection System Irrelevant to the Vehicle Types

Impact of fluorescent whitening agent excitation on White Balance Algorithms

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