Decolorize: Fast, contrast enhancing, color to grayscale conversion

Grundland, Mark; Dodgson, Neil A.

doi:10.1016/j.patcog.2006.11.003

Cited by 230 publications

(195 citation statements)

References 5 publications

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“…In Fig. 2d, the algorithm of Grundland et al [4] generates an augmented image with a very large degree of enhancement (λ = 2.0). Fig.…”

Section: Resultsmentioning

confidence: 99%

“…3, we apply our algorithm to a color image having been used in by Gooch et al [3] and Grundland et al [4]. In contrast to the previous methods, in which "the lettered labels typically are made less legible" [4], Fig.…”

Section: Resultsmentioning

confidence: 99%

“…whole image) computation, where S was the resolution scale of the image. To overcome the slow computation of the optimization techniques, and to capture contrasts in varying-size neighborhoods, random sampling of pixel pairs was explored with a predominant component analysis, to provide fast and direct conversion results [4]. Simply pairing pixels with a Gaussian distribution, as a statistical sampling method, may affect the effectiveness of the contrast acquisition in some cases.…”

Section: Related Workmentioning

confidence: 99%

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Spectral Image Decolorization

Zhao

Tamimi

2010

Advances in Visual Computing

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Abstract. We propose a novel method for decolorizing a color image to grayscale, which preserves the chromatic contrast to be lost if using a simple luminance mapping. Unlike previous work, the conversion performs in spectral domain after applying Fourier transforms on luminance and chromatic channels, and the resultant intensity is recovered from an inverse transform. Frequency spectra inherently provide the magnitude of changes (i.e. contrast) among the channels, in all spatial scales. Consequently, only simple arithmetic operations are needed to add chromatic differences to the final grayscale intensity, leading to realtime computational speed. Moreover, users can flexibly control the decolorization effects by interactively adjusting visually-intuitive parameters, in addition to our predefined values. The method has great potential to be incorporated in commercial software and printer drivers due to its simplicity, speed and repeatability.

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“…In Fig. 2d, the algorithm of Grundland et al [4] generates an augmented image with a very large degree of enhancement (λ = 2.0). Fig.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Spectral Image Decolorization

Zhao

Tamimi

2010

Advances in Visual Computing

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“…YPQ color space was introduced in [7] to convert color to gray scale image by fusing the information from the luminance and chrominance channels. RGB color space can be converted into YPQ color space using (1):…”

Section: A Grey Scale Conversion Methodsmentioning

confidence: 99%

Comparative Study on Subjective Evaluation of Color-to-Grey Conversion Method

2017

IJCCIE

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Abstract-Humans use color not only to interpret or distinguish objects but also to identifying interest r e gi ons for analysis. One of the most well known methods for colorization is Pseudo-coloring; this is a method designed to assign arbitrary colors to grey levels by a fixed mapping. For applying a Pseudo-coloring technique that starts from a color image, a method f o r converting initial colors to grey tones is needed for assisting the conversion process and the conversion need to represent the appearances of the real color from digital images when the conversion finishes. There are many conversions these days but some of those conversions could not present the real color information from images. To avoid this, b e fore beginning Pseudo-coloring, we must p in p oi nt wh ich method is best used for grey transformation. The comparison result then will lead us to implement it to pseudo-color techniques. Here, we report about the results of comparative experiments on color to grey transformations, favoring the YPQ color space.

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“…Albeit they are local functions of the image pixels, they assign different grayscale values to isoluminant colors, altering the luminance information using the chrominance information. Chrominance direct methods can be performed either locally (Smith et al, 2008;Kim et The , 25-27 February 2015, Avila, Spain al., 2009 or globally (Grundland & Dodgson, 2007). Local methods make pixels in the color image not processed in the same way and usually rely on the local chrominance edges for enhancement.…”

Section: Rgb To Graymentioning

confidence: 99%

Advances in Image Pre-Processing to Improve Automated 3d Reconstruction

Ballabeni

Apollonio

Gaiani

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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KEY WORDS: Pre-processing, Filtering, Matching, Automation, Denoising, RGB to Gray, Color, 3D reconstruction ABSTRACT Tools and algorithms for automated image processing and 3D reconstruction purposes have become more and more available, giving the possibility to process any dataset of unoriented and markerless images. Typically, dense 3D point clouds (or texture 3D polygonal models) are produced at reasonable processing time. In this paper, we evaluate how the radiometric pre-processing of image datasets (particularly in RAW format) can help in improving the performances of state-of-the-art automated image processing tools. Beside a review of common pre-processing methods, an efficient pipeline based on color enhancement, image denoising, RGB to Gray conversion and image content enrichment is presented. The performed tests, partly reported for sake of space, demonstrate how an effective image pre-processing, which considers the entire dataset in analysis, can improve the automated orientation procedure and dense 3D point cloud reconstruction, even in case of poor texture scenarios.

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Decolorize: Fast, contrast enhancing, color to grayscale conversion

Cited by 230 publications

References 5 publications

Spectral Image Decolorization

Spectral Image Decolorization

Comparative Study on Subjective Evaluation of Color-to-Grey Conversion Method

Advances in Image Pre-Processing to Improve Automated 3d Reconstruction

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