Colour Image Enhancement using Perceptual Saturation and Vividness

2019 ◽  
Vol 2019 (1) ◽  
pp. 243-246
Author(s):  
Muhammad Safdar ◽  
Noémie Pozzera ◽  
Jon Yngve Hardeberg
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Test Performance ◽  
Appearance Model ◽  
Colour Space ◽  
Categorical Judgment ◽  
Saturation Scale ◽  
Colour Image ◽  
Colour Appearance ◽  
Future Test

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.

A Colour Appearance Model based on Jzazbz Colour Space

2018 ◽  
Vol 2018 (1) ◽  
pp. 96-101
Author(s):  
Muhammad Safdar ◽  
Jon Y. Hardeberg ◽  
Youn Jin Kim ◽  
Ming Ronnier Luo
Keyword(s):  
Appearance Model ◽  
Colour Space ◽  
Model Based ◽  
Colour Appearance

scholarly journals ZCAM, a colour appearance model based on a high dynamic range uniform colour space

2021 ◽  
Vol 29 (4) ◽  
pp. 6036
Author(s):  
Muhammad Safdar ◽  
Jon Yngve Hardeberg ◽  
Ming Ronnier Luo
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Appearance Model ◽  
Colour Space ◽  
Model Based ◽  
High Dynamic ◽  
Colour Appearance

Using Fuzzy Segmentation for Colour Image Enhancement of Computed Tomography Perfusion Images

2011 ◽  
pp. 253-262
Author(s):  
Martin Tabakov
Keyword(s):  
Computed Tomography ◽  
Image Enhancement ◽  
Brain Perfusion ◽  
Colour Space ◽  
Computed Tomography Perfusion ◽  
Fuzzy Similarity ◽  
Colour Image ◽  
Fuzzy Similarity Relation ◽  
Hsv Colour Space ◽  
Fuzzy Segmentation

This chapter presents a methodology for an image enhancement process of computed tomography perfusion images by means of partition generated with appropriately defined fuzzy relation. The proposed image processing is used to improve the radiological analysis of the brain perfusion. Colour image segmentation is a process of dividing the pixels of an image in several homogenously- coloured and topologically connected groups, called regions. As the concept of homogeneity in a colour space is imprecise, a measure of dependency between the elements of such a space is introduced. The proposed measure is based on a pixel metric defined in the HSV colour space. By this measure a fuzzy similarity relation is defined, which next is used to introduce a clustering method that generates a partition, and so a segmentation. The achieved segmentation results are used to enhance the considered computed tomography perfusion images with the purpose of improving the corresponding radiological recognition.

scholarly journals An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index

2021 ◽  
Vol 9 (7) ◽  
pp. 691
Author(s):  
Kai Hu ◽  
Yanwen Zhang ◽  
Chenghang Weng ◽  
Pengsheng Wang ◽  
Zhiliang Deng ◽  
...  
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Quality Evaluation ◽  
High Efficiency ◽  
Natural Image ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Image Quality Evaluation ◽  
Adversarial Network ◽  
Underwater Image

When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.

Colour image enhancement for Ziehl-Neelsen slide images

2008 ◽  
Author(s):  
A.S.W. Wahab ◽  
M.Y. Mashor ◽  
Zaleha Salleh ◽  
S. A. Abdul Shukor ◽  
N. Abdul Rahim ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Colour Image
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