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Journal articles on the topic 'Colour Enhancement'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

ANDREADIS, L., E. GLAVAS, and Ph TSALIDES. "Image enhancement using colour information." International Journal of Remote Sensing 16, no. 12 (1995): 2285–89. http://dx.doi.org/10.1080/01431169508954556.

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2

Kanika, Kapoor, and Arora Shaveta. "COLOUR IMAGE ENHANCEMENT BASED ON HISTOGRAM EQUALIZATION." Electrical & Computer Engineering: An International Journal (ECIJ) 4, no. 3 (2015): 73–82. https://doi.org/10.5281/zenodo.3593819.

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Histogram equalization is a nonlinear technique for adjusting the contrast of an image using its histogram. It increases the brightness of a gray scale image which is different from the mean brightness of the original image. There are various types of Histogram equalization techniques like Histogram Equalization, Contrast Limited Adaptive Histogram Equalization, Brightness Preserving Bi Histogram Equalization, Dualistic Sub Image Histogram Equalization, Minimum Mean Brightness Error Bi Histogram Equalization, Recursive Mean Separate Histogram Equalization and Recursive Sub Image Histogram Equalization. In this paper, the histogram equalization approach of gray-level images is extended for colour images. The acquired image is converted into HSV (Hue, Saturation, Value). The image is then decomposed into two parts by using exposure threshold and then equalized them independently Over enhancement is also controlled in this method by using clipping threshold. For measuring the performance of the enhanced image, entropy and contrast are calculated.

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3

Dixit, Avadhesh Kumar, Rakesh Kumar Yadav, and Ramapati Mishra. "Contrast Enhancement of Colour Images by Optimized Fuzzy Intensification." International Journal of Electrical and Electronics Research 9, no. 4 (2021): 143–49. http://dx.doi.org/10.37391/ijeer.090408.

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Contrast enhancement is a critical and difficult issue because inappropriate enhancement by existing global image enhancement techniques might result in over or under enhancement. Varying areas of the image that are lighted indicate different shades and contrast in the output images. Projected technique uses local colour correction in the Hue Saturation Luminance (HSL) colour space. To control colour fidelity in initial phase an optimized fuzzy intensification parameters are extracted automatically form fuzzy inference system for that particular image. Finally optimized Fuzzy Intensification parameter constants are used to minimize overexposed and underexposed areas and offers elevated contrast improvement. Several lab test conducted to analyze the effectiveness of the proposed method with existing strategies. Many quality evaluation parameters are evaluated, and findings are compared to some known colour picture contrast enhancement approaches. The produced output comparatively better than many existing techniques which support a moderate measure to visual perception of the processed images.

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4

kareem, Hana H., Hazim G. Daway, and Esraa G. Daway. "Underwater Image Enhancement using Colour Restoration based on YCbCr Colour Model." IOP Conference Series: Materials Science and Engineering 571 (August 8, 2019): 012125. http://dx.doi.org/10.1088/1757-899x/571/1/012125.

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5

Meenakshi, Venkat, K. V. Sreelakshmi, B. Elavunkal Athul, and P. J. Jeshmol. "GAN based Underwater Image Enhancement." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 1997–2002. https://doi.org/10.5281/zenodo.8001748.

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Underwater image enhancement has drawn a lot of attention because of its significance in marine engineering and aquatic robotics. Many techniques for enhancing underwater photographs have been put forth in recent years. Since light propagation underwater and in the atmosphere are different, a particular set of nonlinear visual distortions occur. These distortions are brought on by a variety of factors. Red wavelengths are absorbed in deep water as light travels further, which is why underwater images frequently have a green or blue colour as the dominating hue. Low-contrast, fuzzy, and color-degraded images are the result of such wavelength-dependent attenuation, scattering, and other optical properties of water bodies that cause irregular distortions. The previous CNN-GAN (Generative Adversarial Network) based model for real-time underwater image enhancement is sped up by the upgraded inception model proposed by GAN-Based Underwater Image Enhancement. The suggested model assesses image quality based on its global colour, content, local texture, and style information to construct a perceptual loss function. The dataset being used, called EUVP (Enhancing Underwater Visual Perception), is made up of paired and unpaired collections of underwater images captured by seven distinct cameras under a variety of visibility conditions during maritime explorations and cooperative experiments. The suggested model's accuracy can be increased by learning to enhance and improve underwater image quality from both paired and unpaired training.

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6

Safdar, Muhammad, Noémie Pozzera, and Jon Yngve Hardeberg. "Colour Image Enhancement using Perceptual Saturation and Vividness." Color and Imaging Conference 2019, no. 1 (2019): 243–46. http://dx.doi.org/10.2352/issn.2169-2629.2019.27.43.

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A perceptual study was conducted to enhance colour image quality in terms of naturalness and preference using perceptual scales of saturation and vividness. Saturation scale has been extensively used for this purpose while vividness has been little used. We used perceptual scales of a recently developed colour appearance model based on Jzazbz uniform colour space. A two-fold aim of the study was (i) to test performance of recently developed perceptual scales of saturation and vividness compared with previously used hypothetical models and (ii) to compare performance and chose one of saturation and vividness scales for colour image enhancement in future. Test images were first transformed to Jzazbz colour space and their saturation and vividness were then decreased or increased to obtain 6 different variants of the image. Categorical judgment method was used to judge preference and naturalness of different variants of the test images and results are reported.

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7

Annadurai, Kalaivani. "Exploring the Application of Colour Coding For Quality Health Care." Journal of Comprehensive Health 5, no. 2 (2017): 74–76. http://dx.doi.org/10.53553/jch.v05i02.012.

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Colour coding is defined as the systematic, standard application of color to aid in classification and identification.1 Colour coding in health care is mainly used for better enhancement, differentiation, systematic classification. Colour coding is used in various fields of medicine for different purpose. Techniques used commonly are colour differentiation and colour matching.2Colour coded growth chart is an important tool for the classification of malnutrition and used as a diagnostic aid for Health care workers and educational tool for mother in child care.3 Rapid categorization of victims during mass casualties follows colour coding and decides medical action required. Red tag indicates critical patients demanding immediate action, yellow tag indicates non-ambulatory patients between critical and minor categories requiring urgent action, green tag for ambulatory patients who need minor care and black tag implies expired patients.4

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8

Xu, Zhengya, Hong Wu, Xinghuo Yu, and Bin Qiu. "Colour image enhancement by virtual histogram approach." IEEE Transactions on Consumer Electronics 56, no. 2 (2010): 704–12. http://dx.doi.org/10.1109/tce.2010.5505991.

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9

Savage, Katherine. "Colour Enhancement of Black and White Diagrams." Annals of The Royal College of Surgeons of England 89, no. 8 (2007): 823–24. http://dx.doi.org/10.1308/rcsann.2007.89.8.823.

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10

Kapoor, Kanika, and Shaveta Arora. "Colour Image Enhancement based on Histogram Equalization." Electrical & Computer Engineering: An International Journal 4, no. 3 (2015): 73–82. http://dx.doi.org/10.14810/ecij.2015.4306.

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11

Huu, Phuc Dang, and Phan Xuan Le. "Y2O3:Eu3+ concentration and particle size: their impactsoncolor rendering index and lumen of multi-chip white diodes." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 760–65. https://doi.org/10.11591/ijeecs.v27.i2.pp760-765.

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Aiming at achieving higher colour standard for multi-chip white light- emitting diode (MCW-LEDs) through the enhancement of their color rendering index (CRI), this article worked on and proposed a novel MCW- LED package using a compound of red phosphor Y<sub>2</sub>O<sub>3</sub>:Eu<sup>3+</sup> . The redphosphors were synthesized and applied to build both protective coating andin-cup phosphor configurations. The CRI of MCW-LED which has highcorrelated colour heat of 8,500 K was observed to be heightened to 86. Moreover, the scattering of the light when added the red phosphor Y<sub>2</sub>O<sub>3</sub>:Eu<sup>3+</sup> is demonstrated using Mie-scattering theory, while the influences of the redlight enhancement is analyzed using the law of Lambert-Beer.

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12

Green, Phil, and Peter Nussbaum. "Daltonization by spectral filtering." Electronic Imaging 2020, no. 15 (2020): 237–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.15.color-235.

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Observers with a colour vision deficiency usually find it more difficult to discriminate between red and green colours, due to genetic variation in cone spectral sensitivity. Daltonization methods aim to enhance colour stimuli in order to increase the visual difference between them for such observers. In this work we focus on filtering the stimulus in the reflectance domain prior to conversion to colorimetry. This is hypothesized to enable a more precise tuning of the enhancement to the spectral absorptions of the observer with a colour vision deficiency. A spectral sharpening filter was developed and applied to spectral reflectances for a range of colour pairs which observers with a colour vision deficiency would be expected to find difficult to discriminate. The reflectance pairs were converted to colorimetry and presented on a colour display, where the visual difference between the pairs was evaluated by observers in a psychophysical experiment. Results suggest that a suitable filter can lead to an increase in the difference between the red-green pairs.

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13

Balaguer-Puig, M., A. Molada-Tebar, A. Marqués-Mateu, and J. L. Lerma. "CHARACTERISATION OF INTENSITY VALUES ON TERRESTRIAL LASER SCANNING FOR RECORDING ENHANCEMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W5 (August 18, 2017): 49–55. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w5-49-2017.

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Mapping surveys based on terrestrial laser scanning (TLS) are common nowadays for different purposes such as documentation of cultural heritage assets. The chance to extract relevant information from TLS surveys depends not only on the fast acquisition of XYZ coordinates, but also on the meaningful intensity values of the fired objects. TLS behaviour depends on several known factors such as distance, texture, roughness, colour and albedo. This paper seeks to find out the mathematical relationship between the TLS intensity values and the colorimetric data using a colour chart. In order to do so, objective colour specification based on well-known colour spaces is needed. The approach used here started with scanning a colour chart containing a number of colour patches with known chromatic and reflection characteristics. After several transformations, the results allowed us to characterise the intensity behaviour of a time-of-flight laser scanner. The characterisation of the intensity values are tested indoor on the colour chart and outdoor on an archaeological shelter. Promising results are obtained to enhance the behaviour of the intensity values coming from the TLS.

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14

Vazquez-Corral, Javier, and Graham D. Finlayson. "Coupled Retinex." Color and Imaging Conference 2019, no. 1 (2019): 7–12. http://dx.doi.org/10.2352/issn.2169-2629.2019.27.3.

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Retinex is a colour vision model introduced by Land more than 40 years ago. Since then, it has also been widely and successfully used for image enhancement. However, Retinex often introduces colour and halo artefacts. Artefacts are a necessary consequence of the per channel color processing and the lack of any strong control for controlling the locality of the processing (halos are very local errors). In this paper we relate an input to the corresponding output processed retinex image by using a single shading term which is both spatially varying and smooth and a global colour shift. This coupling dramatically reduces common Retinex artefacts. Coupled Retinex is strongly preferred in preference tests.

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15

Zaheer, Sumbul. "A Triadic Approach for Enhancement of Underwater Images Using Adaptive Colour Correction with Unsharp Masking and CLAHE Implementation." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5580–88. http://dx.doi.org/10.22214/ijraset.2024.62740.

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Abstract: The underwater domain has distinct challenges for capturing and examining images both. This is due to absorption and dispersion of light, which diminishes visual clarity and also distorts colour. In this context, we present an extensive method for enhancing underwater images with the objective of restoring true colours, uplifting contrast, and emphasizing minute details. Adaptive colour correction, detail sharpening, and contrast enhancement techniques drafted for underwater environments are all included in our project. Using objective picture quality standards includes the Underwater Image Quality Measure (UIQM), Underwater Colour Image Quality Evaluation (UCIQE), Patch-based Contrast Quality Index (PCQI), and Image Entropy (IE), we evaluate the effectiveness of our technique. With latent uses in oceanology, archaeology, environmental impact analysis, underwater inspection, and photography, the results show significant evolution in visual accuracy and details extraction.

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16

Dhal, Krishna Gopal, and Sanjoy Das. "Hue preserving colour image enhancement models in RGB colour space without gamut problem." International Journal of Signal and Imaging Systems Engineering 11, no. 2 (2018): 102. http://dx.doi.org/10.1504/ijsise.2018.091885.

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17

Dhal, Krishna Gopal, and Sanjoy Das. "Hue preserving colour image enhancement models in RGB colour space without gamut problem." International Journal of Signal and Imaging Systems Engineering 11, no. 2 (2018): 102. http://dx.doi.org/10.1504/ijsise.2018.10013068.

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18

Lihao, Xu, Xu Qiang, and Ming Ronnier Luo. "Image Enhancement for Colour Deficiency via Gamut Mapping." Color and Imaging Conference 29, no. 1 (2021): 71–76. http://dx.doi.org/10.2352/issn.2169-2629.2021.29.71.

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19

Marshall, Justin, and Sonke Johnsen. "Fluorescence as a means of colour signal enhancement." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1724 (2017): 20160335. http://dx.doi.org/10.1098/rstb.2016.0335.

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Fluorescence is a physico-chemical energy exchange where shorter-wavelength photons are absorbed by a molecule and are re-emitted as longer-wavelength photons. It has been suggested a means of communication in several taxa including flowers, pitcher plants, corals, algae, worms, squid, spiders, stomatopods, fish, reptiles, parrots and humans. The surface or object that the pigment molecule is part of appears to glow due to its setting rather than an actual production of light, and this may enhance both signals and, in some cases, camouflage. This review examines some known uses of fluorescence, mainly in the context of visual communication in animals, the challenge being to distinguish when fluorescence is a functional feature of biological coloration or when it is a by-product of a pigment or other molecule. In general, we conclude that most observations of fluorescence lack enough evidence to suggest they are used in visually driven behaviours. This article is part of the themed issue ‘Animal coloration: production, perception, function and application’.

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20

Koseoglu, D., Y. S. Sezer, and K. Karsli. "Colour gamut enhancement with remote light conversion mechanism." Journal of Physics: Conference Series 961 (January 2018): 012010. http://dx.doi.org/10.1088/1742-6596/961/1/012010.

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21

Liu, N., and H. Yan. "Colour image edge enhancement by two-channel process." Electronics Letters 30, no. 12 (1994): 939–40. http://dx.doi.org/10.1049/el:19940642.

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22

You, Kui, Xinghua Li, Pengfei Yi, et al. "PIC-GAN: Symmetry-Driven Underwater Image Enhancement with Partial Instance Normalisation and Colour Detail Modulation." Symmetry 17, no. 2 (2025): 201. https://doi.org/10.3390/sym17020201.

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This model solves the problems of insufficient global feature attention, colour distortion, low contrast, and blurred details in previous methods by using a symmetric U-Net architecture and two new modules: the partial instance normalisation (PIN) module and the colour detail modulation (CDM) module. PIC-GAN effectively balances texture enhancement and feature retention while restoring colour details, ensuring excellent image quality. Experimental results on several publicly available underwater image datasets demonstrate the effectiveness of PIC-GAN, which improves PSNR by 3.23 dB and SSIM by 0.06 compared to WaterGAN, highlighting the potential of PIC-GAN as a powerful solution for underwater image enhancement tasks.

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23

Wei, M., KW Houser, A. David, and MR Krames. "Colour gamut size and shape influence colour preference." Lighting Research & Technology 49, no. 8 (2016): 992–1014. http://dx.doi.org/10.1177/1477153516651472.

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Two psychophysical experiments were conducted to investigate the effect of colour gamut size and shape on colour preference. In the first, two side-by-side booths were designed to resemble a retail setting with clothing; in the second, a single booth was designed to resemble a restaurant setting, but also included a mirror to permit the observer's evaluation of skin tone. These settings were illuminated with two sets of sources, where, compared to a fixed reference, one set created modest chroma enhancement and the other set created greater increases in object chroma. Within each set, gamut shape varied, meaning different hues were saturated, even though, on average, the spectra created the same average increase in chroma. When objects were unfamiliar, as with the fabrics, all chroma-enhancing spectra were preferred to the fixed reference regardless of the gamut shapes. When familiar objects were present, such as food, observers were more discerning about changes in chroma and hue. We conclude that a graphic of gamut shape is an important adjunct to average measures of colour fidelity and gamut.

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24

Zhu, Dachang. "Underwater Image Enhancement Based on the Improved Algorithm of Dark Channel." Mathematics 11, no. 6 (2023): 1382. http://dx.doi.org/10.3390/math11061382.

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Enhancing underwater images presents a challenging problem owing to the influence of ocean currents, the refraction, absorption and scattering of light by suspended particles, and the weak illumination intensity. Recently, different methods have relied on the underwater image formation model and deep learning techniques to restore underwater images. However, they tend to degrade the underwater images, interfere with background clutter and miss the boundary details of blue regions. An improved image fusion and enhancement algorithm based on a prior dark channel is proposed in this paper based on graph theory. Image edge feature sharpening, and dark detail enhancement by homomorphism filtering in CIELab colour space are realized. In the RGB colour space, the multi-scale retinal with colour restoration (MSRCR) algorithm is used to improve colour deviation and enhance colour saturation. The contrast-limited adaptive histogram equalization (CLAHE) algorithm defogs and enhances image contrast. Finally, according to the dark channel images of the three processing results, the final enhanced image is obtained by the linear fusion of multiple images and channels. Experimental results demonstrate the effectiveness and practicality of the proposed method on various data sets.

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25

Alam, Anzar, Mohd Abdullah, and Ravi Shankar Mishra. "Colour Contrast Enhancement Method by Scaling the DC Coefficients in CIE-LAB Colour Space." International Journal of Computer Applications 97, no. 22 (2014): 1–6. http://dx.doi.org/10.5120/17136-7371.

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26

Wong, Siaw Lang, Raveendran Paramesran, and Seng Huat Ong. "A Review on the Underwater Image Restoration and Enhancement Methods." ASM Science Journal 14 (March 31, 2021): 1–13. http://dx.doi.org/10.32802/asmscj.2020.468.

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Light scattering, as well as light absorption in the water, often cause underwater images to be hazy, poorly contrasted, and dominated by either green or blue colour cast. In this paper, we review some of the state-of-the-art approaches in which specifically designed to enhance the quality of the acquired images. These approaches are able to eliminate the color cast and haziness on the images as well as to improve the image colourfulness and contrast. The characteristics of each of the developed approaches are highlighted, and their performances are evaluated both subjectively and objectively by the quality assessment methods.

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27

Nagajothi and Lakshmi. "Parameter Controlled Virtual Histogram Modification in Colour Image Enhancement." Asian Journal of Computer Science and Technology 1, no. 1 (2012): 98–101. http://dx.doi.org/10.51983/ajcst-2012.1.1.1664.

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A new hybrid image enhancement approach based on the parameter-controlled virtual histogram distribution method can enhance simultaneously the overall contrast and the sharpness of an image. The approach also increases the visibility of specified portions or aspects of the image whilst better maintaining image color. It driven by both global and local processes on luminance and chrominance components of the image. The approach was compared with other well-known image enhancement techniques. The experimental results have shown the superiority of the proposed approach

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28

Dang Huu, Phuc, and Phan Xuan Le. "Y2O3:Eu3+ concentration and particle size: their impacts on color rendering index and lumen of multi-chip white diodes." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 760. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp760-765.

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<p><span><span>Aiming at achieving higher colour standard for multi-chip white light-emitting diode (MCW-LEDs) through the enhancement of their color rendering index (CRI), this article worked on and proposed a novel MCW-LED package using a compound of red phosphor Y<sub>2</sub>O<sub>3</sub>:Eu<sup>3+</sup>. The red phosphors were synthesized and applied to build both protective coating and in-cup phosphor configurations. The CRI of MCW-LED which has high correlated colour heat of 8,500 K was observed to be heightened to 86. Moreover, the scattering of the light when added the red phosphor Y<sub>2</sub>O<sub>3</sub>:Eu<sup>3+ </sup>is demonstrated using Mie-scattering theory, while the influences of the red-light enhancement is analyzed using the law of Lambert-Beer.</span></span></p>

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29

Ertan, Z., B. Korkut, G. Gördük, B. Kulavuz, T. Bakırman, and B. Bayram. "ENHANCEMENT OF UNDERWATER IMAGES WITH ARTIFICIAL INTELLIGENCE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W9-2024 (March 8, 2024): 149–56. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w9-2024-149-2024.

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Abstract. Camera systems using optical sensors have made great progress in recent years in obtaining underwater images. Very high-resolution images can be obtained with underwater cameras. However, it becomes difficult to process the image obtained due to many distorting factors such as inhomogeneous underwater lighting, low contrast, blur, sea snow. For this purpose, image enhancement algorithms are used to minimise the problems in the captured images. The physical characteristics of the underwater environment cause degradation effects that are not found in normal images captured in air. Due to the effects such as colour distortion, low contrast and brightness, blurred details in images captured in this type of environment, the usage area of these images is very limited. This project aims to use artificial intelligence methods for colour enhancement of underwater images. In addition, one of the main objectives of the project is to examine the effect of colour enhancement applied to images on other areas of use. Within the scope of the project, An Underwater Image Enhancement Benchmark Dataset and Beyond Dataset and Large Scale Underwater Image Dataset datasets were used. In our study, U-Shape Transformer architecture and Pix2Pix architecture were tested for their usability for image enhancement. According to the results obtained, Pix2Pix architecture achieved the highest accuracy. Accuracy results were obtained as 22.5884 and 0.8764 for PSNR and SSIM, respectively using UIEB dataset. The accuracy values for LSUI dataset are 25.1010 and 0.8440 for PSNR and SSIM, respectively.

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30

Dvořák, Petr, Pavel Suchý, Eva Straková, and Jana Doležalová. "The effect of a diet supplemented with sea-buckthorn pomace on the colour and viscosity of the egg yolk." Acta Veterinaria Brno 86, no. 3 (2017): 303–8. http://dx.doi.org/10.2754/avb201786030303.

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Sea-buckthorn pomace is a very valuable product which contains not only important nutrients but also high-quality oils. The question addressed in the present study was to what extent the diet containing the sea-buckthorn pomace would affect the viscosity and colour of egg yolk measured in the CIELAB system. The feeding mixture for laying hens was supplemented with 20; 50 and 100 g∙kg-1 of sea-buckthorn pomace. As a result, colour indices of the egg yolk such as L*, a* and b* changed significantly (α = 0.01). The greatest relative enhancement was observed for indicator a* for the red colour. Visually, this corresponds to the more intense orange colour of the egg yolk. The addition of sea-buckthorn pomace to the diet for laying hens resulted in a larger increase in indicator ∆E* (CIE total colour difference) compared to the control group. Colour indicator hab is the only indicator whose value oscillated around that determined for the control group. The addition of sea-buckthorn pomace to the diet resulted in an increase in colour indices a*, b* and C*ab. Indicator ∆E* also increased significantly with an increasing amount of sea-buckthorn pomace in a diet. Egg yolks were darker, had more intense red and yellow colours, and showed lower viscosity which are all features preferred by the consumer.

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31

Zhu, Daqi, Zhiqiang Liu, and Youmin Zhang. "Underwater image enhancement based on colour correction and fusion." IET Image Processing 15, no. 11 (2021): 2591–603. http://dx.doi.org/10.1049/ipr2.12247.

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32

Sang, Haitao, Bo Chen, Shifeng Chen, and Li Yan. "Image enhancement based on skin-colour segmentation and smoothness." International Journal of Computational Vision and Robotics 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijcvr.2021.10036485.

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33

Schaefer, Gerald, Maher I. Rajab, M. Emre Celebi, and Hitoshi Iyatomi. "Colour and contrast enhancement for improved skin lesion segmentation." Computerized Medical Imaging and Graphics 35, no. 2 (2011): 99–104. http://dx.doi.org/10.1016/j.compmedimag.2010.08.004.

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34

Mandal, Soham, Sushmita Mitra, and B. Uma Shankar. "FuzzyCIE: fuzzy colour image enhancement for low-exposure images." Soft Computing 24, no. 3 (2019): 2151–67. http://dx.doi.org/10.1007/s00500-019-04048-6.

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35

Wang, Liqian, Zhihui Wei, Hongyi Liu, and Liang Xiao. "Local brightness adaptive image colour enhancement with Wasserstein distance." IET Image Processing 9, no. 1 (2015): 43–53. http://dx.doi.org/10.1049/iet-ipr.2014.0209.

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36

Hou, Guojia, Zhenkuan Pan, Baoxiang Huang, Guodong Wang, and Xin Luan. "Hue preserving-based approach for underwater colour image enhancement." IET Image Processing 12, no. 2 (2018): 292–98. http://dx.doi.org/10.1049/iet-ipr.2017.0359.

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37

Sang, Haitao, Bo Chen, Shifeng Chen, and Li Yan. "Image enhancement based on skin-colour segmentation and smoothness." International Journal of Computational Vision and Robotics 13, no. 1 (2023): 1. http://dx.doi.org/10.1504/ijcvr.2023.127262.

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38

Li, Yuxuan, and Han Yue. "The Application of Emotional Colours and Their Pattern Elements in Product Packaging." Highlights in Art and Design 6, no. 1 (2024): 48–50. http://dx.doi.org/10.54097/jfqz2426.

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This study aims to explore that in contemporary product packaging design, emotional colour, as an important means of conveying emotions visually, can stimulate consumers' emotional resonance and enhance the attractiveness of product packaging. Through the analysis of the association of different colours in the consumer's psychology, combined with the design of pattern elements, the product packaging is more personalized and emotional characteristics. Reasonable use of emotional colour and pattern elements in product packaging design can effectively enhance product brand awareness and consumer purchase intention. In order to achieve the objective object of the product a transcendence and enhancement, so as to give the product itself a richer emotional value, enhance the competitiveness of the product market, for product promotion and marketing provides a new perspective and strategy.

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Bao, Lihong, Jingmei Nie, Chuanjiang Tang, et al. "Practical teaching of textile color science with integrating art." Journal of Education and Educational Research 2, no. 1 (2023): 10–12. http://dx.doi.org/10.54097/jeer.v2i1.5123.

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The art integration of the Textile Colour Science curriculum means that the practical teaching of the course cannot be limited to scientific and technical experiments, but should be integrated with artistic colour matching and colour expression. The aim of the course should both include strengthen the students' understanding and consolidation of the basic theoretical knowledge of colour composition and the enhancement of the students' aesthetic awareness and artistic creation ability. Only through properly arrange the practical teaching section and impove the teaching method of the course, can the teaching objective be achieved.

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Pérez-Benito, Cristina, Samuel Morillas, Cristina Jordán, and J. Alberto Conejero. "Smoothing vs. sharpening of colour images: Together or separated." Applied Mathematics and Nonlinear Sciences 2, no. 1 (2017): 299–316. http://dx.doi.org/10.21042/amns.2017.1.00025.

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AbstractIt is still a challenge to improve the efficiency and effectiveness of image denoising and enhancement methods. There exists denoising and enhancement methods that are able to improve visual quality of images. This is usually obtained by removing noise while sharpening details and improving edges contrast. Smoothing refers to the case of denoising when noise follows a Gaussian distribution.Both operations, smoothing noise and sharpening, have an opposite nature. Therefore, there are few approaches that simultaneously respond to both goals. We will review these methods and we will also provide a detailed study of the state-of-the-art methods that attack both problems in colour images, separately.

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Mohd Sharif, Nurul Atikah, Nor Hazlyna Harun, and Yuhanis Yusof. "Colour Image Enhancement Model of Retinal Fundus Image for Diabetic Retinopathy Recognition." Journal of Information and Communication Technology 23, no. 2 (2024): 293–334. http://dx.doi.org/10.32890/jict2024.23.2.5.

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Diabetic retinopathy (DR) features are typically identified through ophthalmologist eye examinations, but these images often facechallenges like low contrast, non-uniform illumination, and colour inconsistency, affecting the diagnosis accuracy. Therefore, this studyintroduces two novel techniques to improve image quality. One is applying colour image processing techniques to original retinalfundus images, overcoming existing algorithm limitations. Firstly, a new colour correction algorithm was proposed based on TunedBrightness Controlled Single-Scale Retinex (TBCSSR) named Fuzzy TBCSSR Histogram Matching (fTBCSSRhm) to address the issue of colour inconsistency in the dataset. Secondly, based on hybrid particle swarm optimisation-contrast stretch (HPSOCS), the hybridof TBCSSR and HPSOCS named eTBCSSR-HPSOCS algorithm is introduced to tackle the limitations of the standard Particle SwarmOptimisation (PSO) algorithm in HPSOCS, which is prone to local optima and exhibits low convergence rates. This technique combinesthe L-component of the LAB colour model with an enhanced velocity mechanism in PSO and contrast stretching (lavHPSOCS). Its goalis to fine-tune parameters automatically, reduce over-enhancement, avoid unwanted artefacts, and preserve intricate details. This approach improves optimisation by balancing exploration and exploitation and refining velocity control. The proposed algorithm underwent both qualitative and quantitative evaluations. Tests on 100 retinal fundus images from primary datasets were performed to benchmark the algorithm against three existing approaches. The results show that the qualitative performance of the proposed enhancement is more favourable to ophthalmologist specialists than other images. Quantitatively, eTBCSSR-HPSOCS outperforms others with the lowest mean squared error (MSE) of 42.72859, the highest peak signal-to-noise ratio (PSNR) of 32.768, and entropy of 0.977.

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Witlicki, Edward H., Steffen Bähring, Carsten Johnsen, et al. "Enhanced detection of explosives by turn-on resonance Raman upon host–guest complexation in solution and the solid state." Chemical Communications 53, no. 79 (2017): 10918–21. http://dx.doi.org/10.1039/c7cc06517e.

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Abraham, Noor Jabbar, Hazim G. Daway, and Rafid Abbas Ali. "Enhancement of Images with Very Low Light by Using Modified Brightness Low Lightness Areas Algorithm Based on Sigmoid Function." Traitement du Signal 39, no. 4 (2022): 1323–30. http://dx.doi.org/10.18280/ts.390425.

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Enhancement of images with very low light has become an important role in the field of digital image processing, especially during night photography, tracking and medical imaging using binoculars. In this study, a new algorithm was proposed to enhance images with very low light on the basis of the development of brightness low lightness areas algorithm with the treatment of lighting component (Y) by using Sigmoid function in accordance with YIQ colour space. The proposed method was compared with several algorithms as (contrast enhancement approach, multi-scale retinax with color restoration, histogram equalization, fuzzy logic based-on sigmoid membership function, second-order Taylor series approximation and parallel nonlinear adaptive enhancement) by using non-reference quality measures on the basis of LIME data. Results showed the success of the proposed method on improving images with very low light, obtaining the best quality values rates of Entropy (6.81), NIQE (3.46) and PIQE (35.87).

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Meisheng, Cao, Mi Desheng, Pu Yinbin, and Liu Jinghaung. "Application of nonlinear colour enhancement on transparencies for interpretation of glacier surface characteristics." Annals of Glaciology 16 (1992): 190–92. http://dx.doi.org/10.3189/1992aog16-1-190-192.

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According to the analysis of grey scale range on MSS-4, -5, -6 and -7 channel image films for five snow-ice categories on glacier surface, the grey scale among snow, bare ice, ice pinnacle, moraine-covered ice surface and gully bed has been spread nonlinearly by using duplicative processing on high-contrast film. As a result of the rescaling of grey levels, the colour differences of morphological features of Rongbu Glacier in the Qpmolangma region have been increased on false colour composite photography. It is also shown that using MSS-6 to composite false colour images compared to MSS-5 will supply more information for the interpretation of the glacier area.

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Meisheng, Cao, Mi Desheng, Pu Yinbin, and Liu Jinghaung. "Application of nonlinear colour enhancement on transparencies for interpretation of glacier surface characteristics." Annals of Glaciology 16 (1992): 190–92. http://dx.doi.org/10.1017/s026030550000505x.

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According to the analysis of grey scale range on MSS-4, -5, -6 and -7 channel image films for five snow-ice categories on glacier surface, the grey scale among snow, bare ice, ice pinnacle, moraine-covered ice surface and gully bed has been spread nonlinearly by using duplicative processing on high-contrast film. As a result of the rescaling of grey levels, the colour differences of morphological features of Rongbu Glacier in the Qpmolangma region have been increased on false colour composite photography. It is also shown that using MSS-6 to composite false colour images compared to MSS-5 will supply more information for the interpretation of the glacier area.

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Menakadevi, T., J. Arivudainambi, and M. Sulochana. "Multi-Resolution Transforms Based Robust Image Enhancement for High Frequency Colour Images." Advanced Materials Research 933 (May 2014): 762–67. http://dx.doi.org/10.4028/www.scientific.net/amr.933.762.

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An Image Resolution Enhancement Technique based on Interpolation of the high frequency sub-band of colour images obtained by Discrete Wavelet Transform and the input colour image is proposed in this paper. Interpolation determines the intermediate values on the basis of observed values. One of the commonly used interpolation technique is Bicubic Interpolation. The edges are enhanced by introducing an intermediate stage by using Stationary Wavelet Transform. It is designed to overcome the lack of Translation-Invariance of Discrete Wavelet Transform. This is widely used in Signal Denoising and Pattern Recognition. Discrete Wavelet Transform is applied in order to decompose an input colour image into different sub-bands. Then the high frequency sub-bands as well as the input colour image are interpolated separately. The interpolated high frequency sub-bands and the Stationary Wavelet Transform high frequency sub-bands have the same size which means they can be added with each other. The new corrected high frequency sub-bands can be interpolated further for higher enlargement. Then all these sub-bands are combined with interpolated input image for new high resolution image by using Inverse Discrete Wavelet Transform. This has been done by MATLAB. The Peak Signal-Noise Ratio was obtained upto 5dB greater than the conventional and state-of-art image resolution enhancement techniques.

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Li, Yang, Pengzhan Luo, and Yang Zhou. "Loosening detection method for hexagonal screws based on error compensation and deep learning." Insight - Non-Destructive Testing and Condition Monitoring 66, no. 10 (2024): 605–14. http://dx.doi.org/10.1784/insi.2024.66.10.605.

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Screws are extensively utilised across various domains. However, the problem of screw loosening not only impacts the stability and safety of engineering structures but also poses the risk of severe accidents and losses. The hollow design of hexagonal screw heads limits the effectiveness of conventional methods for detecting screw loosening. Additionally, traditional visual detection methods are influenced by the surrounding environment, affecting detection accuracy. To tackle these challenges, this study introduces a novel approach based on deep learning for detecting loosened hexagonal screws. This method primarily relies on the you only look once version 8 (YOLOv8) algorithm to accurately detect the coordinates of the four key points of the hexagonal screw. By applying geometric imaging theory, equations are derived for calculating the loosening angle and length of hexagonal screws. By utilising these equations along with bolt parameters, the degree of loosening is determined. Furthermore, photos captured in low-light environments are enhanced using an improved automatic colour enhancement (ACE) algorithm, which saturates image colours to improve environmental adaptability. This enhancement facilitates better recognition of hexagonal screws even in dark environments. Four colour enhancement methods are also evaluated based on four criteria. Moreover, by employing a back-propagation (BP) neural network for error compensation, the proposed method brings predicted values closer to actual values. The experimental results demonstrate an angular identification error of less than 1° and a length identification error of less than 1 mm for loose hexagonal screws.

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Hogan, David T., and Todd C. Sutherland. "Simple and modular design platform of bimodal turn-on chemodosimeters for oxophilic metal cations." New Journal of Chemistry 42, no. 20 (2018): 16469–73. http://dx.doi.org/10.1039/c8nj04198a.

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Baranitharan, K., and S. Srinivasa Rao Madane. "Clustering Approach for Colour Image Enhancement using Stationary Wavelet Transformation." International Journal of Computer Applications 118, no. 25 (2015): 5–8. http://dx.doi.org/10.5120/20960-3495.

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Hanmandlu, M., S. Arora, G. Gupta, and L. Singh. "Underexposed and overexposed colour image enhancement using information set theory." Imaging Science Journal 64, no. 6 (2016): 321–33. http://dx.doi.org/10.1080/13682199.2016.1215063.

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