Use and analysis of color models in image processing

The theoretical outcomes and experimental results of new color model implemented in algorithms and software of image processing are presented in the paper. This model, as it will be shown below, may be used in modern real time video processing applications such as radar tracking and communication systems. The developed model allows carrying out the image process with the least time delays (i.e. it speeding up image processing). The proposed model can be used to solve the problem of true color object identification Experimental results show that the time spent during RGI color model conversion may approximately four times less than the time spent during other similar models. .

This paper presents the comparative analysis of various color space models like RGB, HSV and YC b C r color models for detection and extraction of human skin from it. Skin detection is broadly used in applications like face detection, tracking of the body parts and hand gesture analysis, for retrieval of skin and blocking of unpleasant contents. The proposed method is performed in four phases; the effect of color space transformation on skin detection performance and finding the appropriate color space for skin detection; the role of the illuminance part of a color space; the appropriate pixel based skin color modeling technique; and finally, the effect of color fidelity algorithms on color based skin classification. The heterogeneity of RGB color space is detached by using HSV and YC b C r color space models. In this paper the input image captured from a camera is converted into three different color space models i.e. RGB, HSV and YC b C r and then performance analysis of algorithms is to be evaluated on the basis of skin detection and extraction.

While emphasizing the intensity or saturation component in order to obtain high-quality color images, keeping the hue component unchanged is important; thus, perceptual color models such as HSI and HSV were used. Hue-Saturation-Intensity (HSI) is a public color model, and many color applications are commonly based on this model. However, the transformation from the HSI model to RGB model after the processing intensity and saturation in the HSI model usually generates the gamut problem, because, the HSI gamut is larger than the RGB gamut. Nevertheless, the common area of RGB gamut and HSI gamut is large to the middle value of the intensity. In this paper, we propose a new color image processing scheme in HSI color model. In the proposed scheme, the positive image processing and the negative image processing are performed in parallel. Since we can realize the negative processing system which is equivalent to the normal (i.e., positive) processing system, the proposed scheme is same degree-of-freedom of the normal processing scheme. In the negative image processing, color images are treated as CMY color space. The common area of CMY gamut and HSI gamut is large from the middle value of the intensity. Therefore, if the intensity value until middle, we choose the positive image processing result as the final result of our scheme. On the other hand, if the intensity value larger than middle value the result of negative image processing result is chosen as the final result. Due to that, we can almost avoid the gamut problem by the proposed scheme.

Applications of Noncausal Gauss–Markov Random Field Models in Image and Video Processing

Mathematical morphology is a powerful tool for image processing and analysis of binary, greyscale and colour images. In the paper, an efficient new fuzzy model for morphological colour image processing is introduced. A new vector-ordering scheme that uses fuzzy if–then rules is proposed, and then the basic morphological operations of erosion and dilation are defined. The morphological operators presented, which are vector preserving, are less sensitive to image distortion and perform significantly better in noise removal problems than other reported morphological operators. Experimental results demonstrate these advantageous characteristics on real images. Additionally, illustrative examples that exhibit the applicability of the proposed framework to edge-detection problems are given.

: Neurodegenerative diseases can make life difficult and lead to death in many cases. They also can be difficult, time-consuming, and costly to diagnose with enough accuracy/certainty. Artificial intelligence (AI) has shown promise in tackling some of the challenges present in medical imaging and is anticipated to become a crucial tool in health care applications in the near future. In particular, deep learning methods have displayed great performance in various subfields of image processing, including but not limited to image segmentation, image synthesis, and image reconstruction. In this paper, many state-of-the-art applications of deep learning models in image processing were reviewed.

This paper explores the use Mathematical models play a crucial role in image processing, offering a powerful framework for analysing, manipulating, and understanding digital images.This abstract emphasizes the significance of mathematical models in image processing and their potential to enhance accuracy and efficiency in this domain.Image processing techniques aim to extract meaningful information from images, enabling applications such as object recognition, medical imaging, and video surveillance.However, raw image data often contain noise, irrelevant details, or complex patterns that hinder accurate interpretation.

The study aimed to apply to Machine Learning (ML) researchers working in image processing and biomedical analysis who play an extensive role in comprehending and performing on complex medical data, eventually improving patient care. Developing a novel ML algorithm specific to Diabetic Retinopathy (DR) is a challenge and need of the hour. Biomedical images include several challenges, including relevant feature selection, class variations, and robust classification. Although the current research in DR has yielded favourable results, several research issues need to be explored. There is a requirement to look at novel pre-processing methods to discard irrelevant features, balance the obtained relevant features, and obtain a robust classification. This is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classifier (SKPD-PSC) method. The novelty of this method relies on the appropriate non-linear classification of exclusive image processing models in harmony with the Platt Scale Classifier (PSC) to improve the accuracy of DR detection. First, a Steerable Filter Kernel Pre-processing (SFKP) model is applied to the Retinal Images (RI) to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians (DDG). Next, the Partial Derivative Image Localization (PDIL) model is applied to the extracted features to localize candidate features and suppress the background noise. Finally, a Platt Scale Classifier (PSC) is applied to the localized features for robust classification. For the experiments, we used the publicly available DR detection database provided by Standard Diabetic Retinopathy (SDR), called DIARETDB0. A database of 130 image samples has been collected to train and test the ML-based classifiers. Experimental results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accuracy rate with minimum time and complexity compared to the state-of-the-art methods. The accuracy and speed of DR detection for numerous types of images will be tested through experimental evaluation. Compared to state-of-the-art methods, the method increases DR detection accuracy by 24% and DR detection time by 37.

According to high development of digital technologies, image processing is more and more import in various fields, such as robot navigator, images classifier. Current image processing model still need large amount of training data to tune processing model and can’t process large images effectively. The recognition successful rate was still not very satisfied. Therefore, this paper researched saliency prior based image processing model, present the Gaussian mixture process and design the feature point based classifier, and then evaluate the model by supervised learning process. Finally, a set of experiments were designed to demonstrate the effectiveness of this paper designed saliency prior based image processing model. The result shows the model works well with a better performance in accurate classification and lower time consumption

: We are developing a general theory for multi scale signal and image modeling, processing, and analysis that matched to singularity-rich data, such as transients and images with edges. Using a linguistic analogy, our model can be interpreted as grammars that constrain the wavelet vocabulary. Our investigation focuses on probabilistic graph models (tree-based hidden Markov models) that can accurately, realistically, and efficiently represent singularity structure in the wavelet domain. Grammar design is being guided by a detailed study of the final structure of singularities using Besov spaces and multifractal analysis.

Image’s edge detection and extraction play an important role in image processing. While conducting edge detection on color images, in order to get better effects and consistent with human visual perception, a fresh HI*S* model was obtained in the process of improving the HIS color model. Edge detection has been made on image which based on HIS color space and the new HI*S* color space through Matlab software, combined with Roberts, Prewitt and Sobel detection operator and using the vector gradient operator. Experimental results show that the proposed method effectively improves the accuracy of edge detection.

In spatial data modeling, the topological notion “continuous function” can link the spatial data with the modeled real world. In this article, we study fuzzy weakly continuous function and fuzzy weakly irresolute functions from set theoretic point of view. These generalizations of continuous functions in fuzzy setting will bring new practically relevant models in image processing.

This chapter introduces applications of visual attention models in image processing related areas: just noticeable difference (JND) modelling, visual quality assessment, image and video coding, visual signal retargeting and compressive sensing. Section 8.1 illustrates the combination of visual attention and the JND model towards a complete visibility threshold model which can be used in a wide spectrum of uses. The application of attention models in quality assessment (QA) of images and video is presented in Section 8.2, and a typical QA index, SSIM, is weighted by visual attention in different ways. The use of attention models to explore visual redundancy for better image coding is introduced in Section 8.3. The applications for image retargeting and compressive sensing are presented in Sections 8.4 and 8.5, respectively.

Regularization is a key element of variational models in image processing. To overcome the weakness of models based on total variation, various high order (typically second order) regularization models have been proposed and studied recently. Among these, Euler's elastica energy based regularizer is perhaps the most interesting in terms of both mathematical and physical justifications. More importantly its success has been proven in applications; however it has been a major challenge to develop fast and effective algorithms. In this paper we propose a new idea for deriving a primal dual algorithm, based on Legendre–Fenchel transformations, for representing the elastica regularizer. Combined with an augmented Lagrangian for-mulation, we are able to derive an equivalent unconstrained optimization that has fewer variables to work with than previous works based on splitting methods. We shall present our algorithms for both the image restoration problem and the image segmentation model. The idea applies to other models where the elastica regularizer is required. Numerical experiments show that the proposed method can produce highly competitive results with better efficiency.

The presence of eyeglasses in facial images poses challenges for image processing, particularly in facial recognition. This paper introduces the remove glasses diffusion model (RGDM), a conditioned denoising diffusion probabilistic model (DDPM) designed for precise glasses removal. RGDM employs conditional modeling to focus on the glasses region while seamlessly restoring facial features. An eyes position accuracy mechanism, leveraging facial landmarks, ensures accurate eye restoration post-removal. Comprehensive evaluations on the CelebA dataset demonstrate RGDM’s superior performance, achieving the lowest Fréchet inception distance (FID) of 27.09 and learned perceptual image patch similarity (LPIPS) of 0.299, outperforming state-of-the-art methods such as 3D synthetic, cycleconsistent generative adversarial network (CycleGAN), and eyeglasses removal generative adversarial network (ERGAN). These results highlight the model’s effectiveness in producing natural and high-fidelity facial reconstructions. This work advances glasses removal technology and underscores the significance of conditional models in image processing. The proposed approach has practical implications for facial recognition and image enhancement, paving the way for more accurate and robust real-world applications.