Topic Editors

National Research Council, Institute for the Applications of Calculus "M. Picone", Via P. Castellino 111, 80131 Naples, Italy
National Research Council, Institute for the Applications of Calculus "M. Picone", Via P. Castellino 111, 80131 Naples, Italy

Color Image Processing: Models and Methods (CIP: MM)

Abstract submission deadline
30 May 2024
Manuscript submission deadline
30 July 2024
Viewed by
11254

Topic Information

Dear Colleagues,

Color information plays a crucial role in digital image processing since it is a robust descriptor that can often improve data compression and simplify scene understanding for humans and automatic vision systems. Research about color presents new challenges since it makes it possible to expand the currently available methods, most of which are limited to the gray-level class of images. Furthermore, the multivariate nature of color image data requires the design of appropriate models and methods at both the mathematical and percentual/computational levels. As a result, Color Image Processing (CIP) has become an active research area witnessed by many papers during the past two decades. It finds wide application in numerous fields such as, among many others, Agriculture, Biomedicine, Cultural Heritage, Remote Sensing, Defense, and Security.

This Topic aims to give an overview of the state-of-the-art in color image processing and provide present/future directions in several applicative contexts. Specifically, the Topic focuses on two aspects that traditionally are considered separately: mathematical modeling and computational design of methods. Papers presenting reviews, alternative perspectives, new models/methods in the field of CIP facing both these aspects are welcome. All submitted papers will be peer-reviewed and selected on the basis of both their quality and relevance to the theme of this Topic.

We invite original contributions that provide novel solutions to these challenging problems. Submitted papers can address theoretical or practical aspects of progress and directions in CIP.

Issues of interest include, but are not limited to:

  • Information Theory and Entropy-based method for CIP
  • Color space models
  • Mathematical modeling for CIP
  • Numerical approximation for CIP
  • Color image enhancement, segmentation, and resizing
  • Data augmentation for CIP
  • Deep learning for CIP
  • Color content-based image retrieval
  • Color quality image assessment
  • Biometric CIP
  • Color medical imaging
  • CIP Models and Methods applied to Agriculture, Cultural Heritage, Remote Sensing, Defense, and Security

Dr. Giuliana Ramella
Dr. Isabella Torcicollo
Topic Editors

Keywords

  • color images
  • mathematical models
  • computational methods
  • color visual processing

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400 Submit
Computation
computation
2.2 3.3 2013 18 Days CHF 1800 Submit
Entropy
entropy
2.7 4.7 1999 20.8 Days CHF 2600 Submit
Journal of Imaging
jimaging
3.2 4.4 2015 21.7 Days CHF 1800 Submit

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Published Papers (7 papers)

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17 pages, 7440 KiB  
Article
Multi-Scale Cross-Attention Fusion Network Based on Image Super-Resolution
by Yimin Ma, Yi Xu, Yunqing Liu, Fei Yan, Qiong Zhang, Qi Li and Quanyang Liu
Appl. Sci. 2024, 14(6), 2634; https://doi.org/10.3390/app14062634 - 21 Mar 2024
Viewed by 619
Abstract
In recent years, deep convolutional neural networks with multi-scale features have been widely used in image super-resolution reconstruction (ISR), and the quality of the generated images has been significantly improved compared with traditional methods. However, in current image super-resolution network algorithms, these methods [...] Read more.
In recent years, deep convolutional neural networks with multi-scale features have been widely used in image super-resolution reconstruction (ISR), and the quality of the generated images has been significantly improved compared with traditional methods. However, in current image super-resolution network algorithms, these methods need to be further explored in terms of the effective fusion of multi-scale features and cross-domain application of attention mechanisms. To address these issues, we propose a novel multi-scale cross-attention fusion network (MCFN), which optimizes the feature extraction and fusion process in structural design and modular innovation. In order to make better use of the attention mechanism, we propose a Pyramid Multi-scale Module (PMM) to extract multi-scale information by cascading. This PMM is introduced in MCFN and is mainly constructed by multiple multi-scale cross-attention modules (MTMs). To fuse the feature information of PMMs efficiently in both channel and spatial dimensions, we propose the cross-attention fusion module (CFM). In addition, an improved integrated attention enhancement module (IAEM) is inserted at the network’s end to enhance the correlation of high-frequency feature information between layers. Experimental results show that the algorithm significantly improves the reconstructed images’ edge information and texture details, and the benchmark dataset’s performance evaluation shows comparable performance to current state-of-the-art techniques. Full article
(This article belongs to the Topic Color Image Processing: Models and Methods (CIP: MM))
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21 pages, 22780 KiB  
Article
Ref-MEF: Reference-Guided Flexible Gated Image Reconstruction Network for Multi-Exposure Image Fusion
by Yuhui Huang, Shangbo Zhou, Yufen Xu, Yijia Chen and Kai Cao
Entropy 2024, 26(2), 139; https://doi.org/10.3390/e26020139 - 3 Feb 2024
Viewed by 1049
Abstract
Multi-exposure image fusion (MEF) is a computational approach that amalgamates multiple images, each captured at varying exposure levels, into a singular, high-quality image that faithfully encapsulates the visual information from all the contributing images. Deep learning-based MEF methodologies often confront obstacles due to [...] Read more.
Multi-exposure image fusion (MEF) is a computational approach that amalgamates multiple images, each captured at varying exposure levels, into a singular, high-quality image that faithfully encapsulates the visual information from all the contributing images. Deep learning-based MEF methodologies often confront obstacles due to the inherent inflexibilities of neural network structures, presenting difficulties in dynamically handling an unpredictable amount of exposure inputs. In response to this challenge, we introduce Ref-MEF, a method for color image multi-exposure fusion guided by a reference image designed to deal with an uncertain amount of inputs. We establish a reference-guided exposure correction (REC) module based on channel attention and spatial attention, which can correct input features and enhance pre-extraction features. The exposure-guided feature fusion (EGFF) module combines original image information and uses Gaussian filter weights for feature fusion while keeping the feature dimensions constant. The image reconstruction is completed through a gated context aggregation network (GCAN) and global residual learning GRL. Our refined loss function incorporates gradient fidelity, producing high dynamic range images that are rich in detail and demonstrate superior visual quality. In evaluation metrics focused on image features, our method exhibits significant superiority and leads in holistic assessments as well. It is worth emphasizing that as the number of input images increases, our algorithm exhibits notable computational efficiency. Full article
(This article belongs to the Topic Color Image Processing: Models and Methods (CIP: MM))
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28 pages, 35527 KiB  
Article
Nighttime Image Stitching Method Based on Image Decomposition Enhancement
by Mengying Yan, Danyang Qin, Gengxin Zhang, Huapeng Tang and Lin Ma
Entropy 2023, 25(9), 1282; https://doi.org/10.3390/e25091282 - 31 Aug 2023
Viewed by 1021
Abstract
Image stitching technology realizes alignment and fusion of a series of images with common pixel areas taken from different viewpoints of the same scene to produce a wide field of view panoramic image with natural structure. The night environment is one of the [...] Read more.
Image stitching technology realizes alignment and fusion of a series of images with common pixel areas taken from different viewpoints of the same scene to produce a wide field of view panoramic image with natural structure. The night environment is one of the important scenes of human life, and the night image stitching technology has more urgent practical significance in the fields of security monitoring and intelligent driving at night. Due to the influence of artificial light sources at night, the brightness of the image is unevenly distributed and there are a large number of dark light areas, but often these dark light areas have rich structural information. The structural features hidden in the darkness are difficult to extract, resulting in ghosting and misalignment when stitching, which makes it difficult to meet the practical application requirements. Therefore, a nighttime image stitching method based on image decomposition enhancement is proposed to address the problem of insufficient line feature extraction in the stitching process of nighttime images. The proposed algorithm performs luminance enhancement on the structural layer, smoothes the nighttime image noise using a denoising algorithm on the texture layer, and finally complements the texture of the fused image by an edge enhancement algorithm. The experimental results show that the proposed algorithm improves the image quality in terms of information entropy, contrast, and noise suppression compared with other algorithms. Moreover, the proposed algorithm extracts the most line features from the processed nighttime images, which is more helpful for the stitching of nighttime images. Full article
(This article belongs to the Topic Color Image Processing: Models and Methods (CIP: MM))
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19 pages, 17842 KiB  
Article
Order Space-Based Morphology for Color Image Processing
by Shanqian Sun, Yunjia Huang, Kohei Inoue and Kenji Hara
J. Imaging 2023, 9(7), 139; https://doi.org/10.3390/jimaging9070139 - 7 Jul 2023
Cited by 1 | Viewed by 1237
Abstract
Mathematical morphology is a fundamental tool based on order statistics for image processing, such as noise reduction, image enhancement and feature extraction, and is well-established for binary and grayscale images, whose pixels can be sorted by their pixel values, i.e., each pixel has [...] Read more.
Mathematical morphology is a fundamental tool based on order statistics for image processing, such as noise reduction, image enhancement and feature extraction, and is well-established for binary and grayscale images, whose pixels can be sorted by their pixel values, i.e., each pixel has a single number. On the other hand, each pixel in a color image has three numbers corresponding to three color channels, e.g., red (R), green (G) and blue (B) channels in an RGB color image. Therefore, it is difficult to sort color pixels uniquely. In this paper, we propose a method for unifying the orders of pixels sorted in each color channel separately, where we consider that a pixel exists in a three-dimensional space called order space, and derive a single order by a monotonically nondecreasing function defined on the order space. We also fuzzify the proposed order space-based morphological operations, and demonstrate the effectiveness of the proposed method by comparing with a state-of-the-art method based on hypergraph theory. The proposed method treats three orders of pixels sorted in respective color channels equally. Therefore, the proposed method is consistent with the conventional morphological operations for binary and grayscale images. Full article
(This article belongs to the Topic Color Image Processing: Models and Methods (CIP: MM))
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18 pages, 59354 KiB  
Article
Enhanced CycleGAN Network with Adaptive Dark Channel Prior for Unpaired Single-Image Dehazing
by Yijun Xu, Hanzhi Zhang, Fuliang He, Jiachi Guo and Zichen Wang
Entropy 2023, 25(6), 856; https://doi.org/10.3390/e25060856 - 26 May 2023
Cited by 2 | Viewed by 1495
Abstract
Unpaired single-image dehazing has become a challenging research hotspot due to its wide application in modern transportation, remote sensing, and intelligent surveillance, among other applications. Recently, CycleGAN-based approaches have been popularly adopted in single-image dehazing as the foundations of unpaired unsupervised training. However, [...] Read more.
Unpaired single-image dehazing has become a challenging research hotspot due to its wide application in modern transportation, remote sensing, and intelligent surveillance, among other applications. Recently, CycleGAN-based approaches have been popularly adopted in single-image dehazing as the foundations of unpaired unsupervised training. However, there are still deficiencies with these approaches, such as obvious artificial recovery traces and the distortion of image processing results. This paper proposes a novel enhanced CycleGAN network with an adaptive dark channel prior for unpaired single-image dehazing. First, a Wave-Vit semantic segmentation model is utilized to achieve the adaption of the dark channel prior (DCP) to accurately recover the transmittance and atmospheric light. Then, the scattering coefficient derived from both physical calculations and random sampling means is utilized to optimize the rehazing process. Bridged by the atmospheric scattering model, the dehazing/rehazing cycle branches are successfully combined to form an enhanced CycleGAN framework. Finally, experiments are conducted on reference/no-reference datasets. The proposed model achieved an SSIM of 94.9% and a PSNR of 26.95 on the SOTS-outdoor dataset and obtained an SSIM of 84.71% and a PSNR of 22.72 on the O-HAZE dataset. The proposed model significantly outperforms typical existing algorithms in both objective quantitative evaluation and subjective visual effect. Full article
(This article belongs to the Topic Color Image Processing: Models and Methods (CIP: MM))
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21 pages, 23878 KiB  
Article
Manipulating Pixels in Computer Graphics by Converting Raster Elements to Vector Shapes as a Function of Hue
by Tajana Koren Ivančević, Nikolina Stanić Loknar, Maja Rudolf and Diana Bratić
J. Imaging 2023, 9(6), 106; https://doi.org/10.3390/jimaging9060106 - 23 May 2023
Viewed by 1253
Abstract
This paper proposes a method for changing pixel shape by converting a CMYK raster image (pixel) to an HSB vector image, replacing the square cells of the CMYK pixels with different vector shapes. The replacement of a pixel by the selected vector shape [...] Read more.
This paper proposes a method for changing pixel shape by converting a CMYK raster image (pixel) to an HSB vector image, replacing the square cells of the CMYK pixels with different vector shapes. The replacement of a pixel by the selected vector shape is done depending on the detected color values for each pixel. The CMYK values are first converted to the corresponding RGB values and then to the HSB system, and the vector shape is selected based on the obtained hue values. The vector shape is drawn in the defined space, according to the row and column matrix of the pixels of the original CMYK image. Twenty-one vector shapes are introduced to replace the pixels depending on the hue. The pixels of each hue are replaced by a different shape. The application of this conversion has its greatest value in the creation of security graphics for printed documents and the individualization of digital artwork by creating structured patterns based on the hue. Full article
(This article belongs to the Topic Color Image Processing: Models and Methods (CIP: MM))
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13 pages, 3016 KiB  
Article
Comparing Different Algorithms for the Pseudo-Coloring of Myocardial Perfusion Single-Photon Emission Computed Tomography Images
by Abdurrahim Rahimian, Mahnaz Etehadtavakol, Masoud Moslehi and Eddie Y. K. Ng
J. Imaging 2022, 8(12), 331; https://doi.org/10.3390/jimaging8120331 - 19 Dec 2022
Cited by 1 | Viewed by 1580
Abstract
Single-photon emission computed tomography (SPECT) images can significantly help physicians in diagnosing patients with coronary artery or suspected coronary artery diseases. However, these images are grayscale with qualities that are not readily visible. The objective of this study was to evaluate the effectiveness [...] Read more.
Single-photon emission computed tomography (SPECT) images can significantly help physicians in diagnosing patients with coronary artery or suspected coronary artery diseases. However, these images are grayscale with qualities that are not readily visible. The objective of this study was to evaluate the effectiveness of different pseudo-coloring algorithms of myocardial perfusion SPECT images. Data were collected using a Siemens Symbia T2 dual-head SPECT/computed tomography (CT) scanner. After pseudo-coloring, the images were assessed both qualitatively and quantitatively. The qualities of different pseudo-color images were examined by three experts, while the images were evaluated quantitatively by obtaining indices such as mean squared error (MSE), peak signal-to-noise ratio (PSNR), normalized color difference (NCD), and structure similarity index metric (SSIM). The qualitative evaluation demonstrated that the warm color map (WCM), followed by the jet color map, outperformed the remaining algorithms in terms of revealing the non-visible qualities of the images. Furthermore, the quantitative evaluation results demonstrated that the WCM had the highest PSNR and SSIM but the lowest MSE. Overall, the WCM could outperform the other color maps both qualitatively and quantitatively. The novelty of this study includes comparing different pseudo-coloring methods to improve the quality of myocardial perfusion SPECT images and utilizing our collected datasets. Full article
(This article belongs to the Topic Color Image Processing: Models and Methods (CIP: MM))
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