A fast white balance algorithm based on pixel greyness

Thai, Ba; Deng, Guang; Ross, Robert

doi:10.1007/s11760-016-0990-6

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…The input image is pre-processed by the White-Balance algorithm for colour correction and the contrast of the image is improved using the contrast enhancement technique. White-Balance aims at improving the image aspect, primarily by removing the undesired color castings due to various illumination or medium attenuation properties [11] . White-Balance discusses the visible color white under specific lighting conditions affects the hue of all other colors.…”

Section: Block Diagrammentioning

confidence: 99%

Underwater Image Enhancement Using MIRNet

Muthusamy¹,

Aruna²,

Sonti³

et al. 2023

j. of electron. & inf. syst.

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In recent years, enhancement of underwater images is a challenging task, which is gaining priority since the human eye cannot perceive images under water. The significant details underwater are not clearly captured using the conventional image acquisition techniques, and also they are expensive. Hence, the quality of the image processing algorithms can be enhanced in the absence of costly and reliable acquisition techniques. Traditional algorithms have certain limitations in the case of these images with varying degrees of fuzziness and color deviation. In the proposed model, the authors used a deep learning model for underwater image enhancement. First, the original image is pre-processed by the white balance algorithm for colour correction and the contrast of the image is improved using the contrast enhancement technique. Next, the pre-processed image is given to the MIRNet for enhancement. MIRNet is a deep learning framework that can be used to enhance the low-light level images. The enhanced image quality is measured using peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and structural similarity index (SSIM) parameters.

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Section: Block Diagrammentioning

confidence: 99%

Underwater Image Enhancement Using MIRNet

Muthusamy¹,

Aruna²,

Sonti³

et al. 2023

j. of electron. & inf. syst.

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“…where W is a white-color region within the reference image and #W is the pixels count within W . If no such region is available, one can use a color constancy algorithm, such as [25] to estimate L a . Consequently, using equation (3), the albedo/texture k p d of every scene points (Figure 4-c) can be recovered as:…”

Section: Per-frame Texture Removalmentioning

confidence: 99%

Detecting Specular Reflections and Cast Shadows to Estimate Reflectance and Illumination of Dynamic Indoor Scenes

Jiddi¹,

Robert²,

Marchand

2022

IEEE Trans. Visual. Comput. Graphics

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The goal of Mixed Reality (MR) is to achieve a seamless and realistic blending between real and virtual worlds. This requires the estimation of reflectance properties and lighting characteristics of the real scene. One of the main challenges within this task consists in recovering such properties using a single RGB-D camera. In this paper, we introduce a novel framework to recover both the position and color of multiple light sources as well as the specular reflectance of real scene surfaces. This is achieved by detecting and incorporating information from both specular reflections and cast shadows. Our approach is capable of handling any textured surface and considers both static and dynamic light sources. Its effectiveness is demonstrated through a range of applications including visually-consistent mixed reality scenarios (e.g. correct real specularity removal, coherent shadows in terms of shape and intensity) and retexturing where the texture of the scene is altered whereas the incident lighting is preserved.

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“…Many methods have been developed to detect and restore colour cast images in the literature such as grey world [3, 4], white patch [5, 6], neural network [7, 8], equivalent circle algorithm [9] and so on. Grey world method calculates weighting values in red, green and blue (RGB) colour space for colour correction by matching the colour average with a grey reference value.…”

Section: Introductionmentioning

confidence: 99%

Modified grey world method to detect and restore colour cast images

Liu

Chen

2019

IET Image Processing

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This study proposes a new, simple but effective technique to detect and restore colour cast images, named modified grey world method. This method detects colour cast images of outdoor surveillance videos by computing the values in the YUV colour space, which makes it much easier than classic methods. Specific colour cast can be found out by calculating the hue values. Additionally, this method can detect not only simple colour cast images but also multiple colour cast images simultaneously. To detect and restore a colour cast image, the authors first remove all grey pixels and separate it into multiple parts with a maze-solving algorithm. Then, they compute the YUV colour values of each part. If the values are too high or too low, this part of the input image is designated as a colour cast. Finally, they carry out a restoration procedure, in which they calculate weights by matching average colour value with a grey reference value in YUV colour space. This method has been tested in the Safety City surveillance system in Wuhan city, China. The results show that the proposed method leads to better results in detecting and restoring colour cast imaging than classic methods in outdoor surveillance videos.

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A fast white balance algorithm based on pixel greyness

Cited by 9 publications

References 27 publications

Underwater Image Enhancement Using MIRNet

Underwater Image Enhancement Using MIRNet

Detecting Specular Reflections and Cast Shadows to Estimate Reflectance and Illumination of Dynamic Indoor Scenes

Modified grey world method to detect and restore colour cast images

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