Spatially Adaptive Color Image Processing

This paper aims to outline the General Adaptive Neighborhood Image Processing (GANIP) approach [1–3], which has been recently introduced. An intensity image is represented with a set of local neighborhoods defined for each point of the image to be studied. These so-called General Adaptive Neighborhoods (GANs) are simultaneously adaptive with the spatial structures, the analyzing scales and the physical settings of the image to be addressed and/or the human visual system. After a brief theoretical introductory survey, the GANIP approach will be successfully applied on real application examples in image restoration, enhancement and segmentation.

This paper aims to present a novel framework, entitled General Adaptive Neighborhood Image Processing (GANIP), focusing on the area of adaptive morphology. The usual fixed-shape structuring elements required in Mathematical Morphology (MM) are substituted by adaptive (GAN-based) spatial structuring elements. GANIP and MM results to the so-called General Adaptive Neighborhood Mathematical Morphology (GANMM). Several GANMM-based image filters are defined. They satisfy strong morphological and topological properties such as connectedness. The practical results in the fields of image restoration and image enhancement confirm and highlight the theoretical advantages of the GANMM approach.

The topic of colour image segmentation has been and still is a hot issue in areas such as computer vision and image processing because of its wide range of practical applications. The urge has led to the development of numerous colour image segmentation algorithms to extract salient objects from colour images. However, because of the diverse imaging conditions in varying application domains, accuracy and robustness of several state-of-the-art colour image segmentation algorithms still leave room for further improvement. This dissertation reports on the development of a new image segmentation algorithm based on perceptual colour difference saliency along with binary morphological operations. The algorithm consists of four essential processing stages which are colour image transformation, luminance image enhancement, salient pixel computation and image artefact filtering. The input RGB colour image is first transformed into the CIE L*a*b colour image to achieve perceptual saliency and obtain the best possible calibration of the transformation model. The luminance channel of the transformed colour image is then enhanced using an adaptive gamma correction function to alleviate the adverse effects of illumination variation, low contrast and improve the image quality significantly. The salient objects in the input colour image are then determined by calculating saliency at each pixel in order to preserve spatial information. The computed saliency map is then filtered using the morphological operations to eliminate undesired factors that are likely present in the colour image. A series of experiments was performed to evaluate the effectiveness of the new perceptual colour difference saliency algorithm for colour image segmentation. This was accomplished by testing the algorithm on a large set of a hundred and ninety images acquired from four distinct publicly available benchmarks corporal. The accuracy of the developed colour image segmentation algorithm was quantified using four widely used statistical evaluation metrics in terms of precision, F-measure, error and Dice. Promising results were obtained despite the fact that the experimental images were selected from four different corporal and in varying imaging conditions. The results have indeed demonstrated that the performance of the newly developed colour image segmentation algorithm is consistent with an improved performance compared to a number of other saliency and non- saliency state-of-the-art image segmentation algorithms.

Segmentation of color images is a tricky task. Regaining the segments in an image using the content image is a difficult and significant problem. Medical imaging is the most alive research topic from the past two decades. From the medical diagnosis of patients suffering from various diseases, abnormal regions in the organs can be easily identified, which is a greatest achievement. It is experimentally proved that graph based segmentation methods are better than the other segmentation techniques, especially when combined with statistical methods. We have proposed color image segmentation by adaptive graph cut method in this paper. It consists of two important stages. During the first phase we enhance the input color image using transformation technique as the image may contain noise, may be of low contrast and missing some color statistics. Then this enhanced color image is processed under graph cut technique to get better results, especially for the analysis of medical and general images. The proposed method contributes to medical imaging by means of image segmentation and also to other general image analysis. Our experimental results are found to very good in segmenting color images.

Various nonlinear, fixed-neighborhood techniques for filtering color images based on local statistics have been proposed in the literature. We present adaptive neighborhood filtering (ANF) techniques for noise removal in color images. The main idea is to find for each pixel in the image (called the 'seed' when being processed) a variable-shaped, variable-sized neighborhood that contains only pixels that are similar to the seed. Then, statistics computed using pixels within the adaptive neighborhood are used to derive the filter output. Results of the ANF techniques are compared with those given by a few multivariate fixed-neighborhood filters: the double- window modified trimmed-mean filter, the generalized vector directional filter -- double-window -- (alpha) -trimmed mean filter, the adaptive hybrid multivariate filter, and the adaptive non-parametric filter with Gaussian kernel. It is shown that the ANF techniques provide the best visual results, effectively suppressing noise while not blurring edges. The ANF results are also the best in terms of objective measures (such as normalized mean-squared error and normalized color difference).

As an attempt to introduce interactive, content‐dependent adaptive (ICDA) image processing, a simple but powerful active image sensing and two image enhancement methods are introduced via adaptive CNN‐UM sensor‐computers. Thus, the method ICDA can be used for adaptive control of image sensing and for subsequent on‐line or off‐line image enhancement as well. The algorithms use both intensity and contrast content. The image sensing technology can be realized with the current CNN‐UM chip. Our first image enhancement method is also executable on this chip, but it is more suitable for the adaptive cellular neural network universal machine (ACNN‐UM) architecture. Some results of simulator and chip experiments and an adaptive extended cell are presented. Our second, dynamical image enhancement method is planned to be executable on a multi‐layer, complex cell CNN architecture. In (Proceedings of the 6th IEEE International Workshop on Cellular Neural Networks and their Applications(CNNA‐2000) Catania, 2000; 213–217) 3‐layer architecture is described which is capable of realizing the main part of the second enhancement method.The main issues of our paper are as follows: the novel outlook of the ICDA framework, three new methods for two key application areas of CNN‐UM, the notion of ‘regional’ adaptive computing, the novelty of application of equilibrium‐computing in the third method. However, the key novelty of our work is not just a new method and a new realization: by combining sensing and computing, dynamically and pixelwise, a new quality becomes practical. Copyright © 2002 John Wiley & Sons, Ltd.

Adaptive image processing is one of the most important techniques in visual information processing, especially in early vision such as image restoration, filtering, enhancement, and segmentation. While existing books present some important aspects of the issue, there is not a single book that treats this problem from a viewpoint that is directly li

The increasing demand for intelligent surveillance systems has led to the integration of Internet of Things (IoT) technology with advanced image processing techniques to enhance security applications. This paper presents a novel framework that combines IoT-enabled devices with adaptive image processing, leveraging the power of edge computing and machine learning to optimize real-time analysis of visual data. Traditional surveillance systems often face limitations such as high latency, bandwidth constraints, and inadequate adaptability to dynamic environments. Our proposed solution addresses these challenges by processing data closer to the source, reducing latency and bandwidth usage while maintaining high processing accuracy. The framework integrates adaptive algorithms for dynamic resolution adjustment, context-aware object detection, and real-time anomaly detection, allowing the system to intelligently adjust based on scene complexity, environmental conditions, and available computational resources. Additionally, the use of machine learning techniques enhances the system’s ability to learn from new patterns and adapt to evolving security threats. Experimental evaluations demonstrate the proposed system’s superior performance in terms of object detection accuracy, processing speed, and energy efficiency compared to traditional centralized surveillance systems. This paper also discusses the implementation of secure communication protocols and privacy-preserving techniques to ensure data security in IoT environments. The proposed framework offers a scalable and flexible solution, making it suitable for a wide range of security applications, from public spaces to private facilities. Future work will focus on improving the system's robustness and extending its capabilities for large-scale deployments.

Image segmentation is the process of dividing an image into homogenous regions. It is an essential step towards high-level image processing task such as image analysis, pattern recognition and computer vision. Processing of color images has become an important issue due to its huge usage in computer vision applications. It is observed that most of the color image segmentation techniques are derived from monochrome image segmentation. The techniques for segmentation of monochrome images are based on the principles of histogram thresholding, edge detection, region growing etc. Many color image segmentation algorithms using different color models and these principles are proposed. Extraction of objects within an image without a prior knowledge is one of the important issues in segmentation area. Novel approaches such as fuzzy set theory, neural network and neuro-fuzzy based segmentation are coming up to tackle this problem. This paper is an endeavor to review various algorithms and recent advances in color image segmentation.

Color image restoration is a fundamental task in computer vision and image processing, with extensive real‐world applications. In practice, color images often suffer from degradations caused by sensor noise, optical blur, compression artifacts, and data loss during the acquisition, transmission, or storage. Unlike grayscale images, color images exhibit high correlations among their RGB channels. Directly extending grayscale restoration methods to color images often leads to issues such as color distortion and structural artifacts. To address these challenges, this paper proposes a novel quaternion‐based color image restoration framework. The method integrates low‐rank pseudo‐norm constraints with saturation‐value total variation (SVTV) regularization, effectively enhancing restoration quality in tasks including denoising, deblurring, and inpainting of degraded color images. The proposed algorithm is efficiently solved using the alternating direction method of multipliers (ADMM), and restoration performance is rigorously evaluated through quantitative metrics including peak signal‐to‐noise ratio (PSNR), structural similarity index measure (SSIM), and S‐CIELAB error. Extensive experimental results demonstrate the superior performance of our method compared to existing approaches.

segmentation divides an image into several constituent components such as color, structure, shape, and texture. It forms a major research topic for many image processing researchers as the applications are endless. Its applications include image enhancement, object detection, image retrieval, image compression, and medical image processing to name a few. The segmentation of color images is necessary for efficient pattern recognition and feature extraction involving various color spaces such as RGB, HSV and CIE L*A*B*, etc. This paper describes the different cluster based segmentation techniques used for segmenting the different color images and the resultant is analyzed with subjective and objective measures. Initially, registered color images are considered as input. Then the cluster based segmentation techniques namely K-Means clustering, Pillar-Kmeans clustering and Fuzzy C-means (FCM) clustering techniques are applied. Further, the segmented image is analyzed with measures such as compactness and execution time. From the experimental results, it has been observed that K-means and Pillar-Kmeans are the most suitable techniques for RGB, HSV and LAB color spaces than the FCM technique.

A novel Adaptive Color Image Segmentation (ACIS) System for color image segmentation is presented. The proposed ACIS system uses a neural network with architecture similar to the multilayer perceptron (MLP) network. The main difference is that neurons here uses a multisigmoid activation function. The multisigmoid function is the key for segmentation. The number of steps i.e. thresholds in the multisigmoid function are dependant on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSV color space. Here, the main use of neural network is to detect the number of objects automatically from an image. The advantage of this method is that no a priori knowledge is required to segment the color image. ACIS label the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images. Experimental results show that the performance of ACIS is robust on noisy images also.

Diabetic retinopathy is the main cause of vision loss among working-age adults around the world. Early detection and timely treatment are essential to reduce impairment and prevent blindness. In order to enable people to easily check their own conditions, we develop a decision support system for detecting diabetic retinopathy in early stage. The system extracts two important features, blood vessels and hard exudates, by using a combination of image processing techniques including black top hat, morphological opening and closing, canny edge detection, blob function, convert label matrix, convex-Hull method and color thresholding. In addition, an adaptive algorithm is proposed to cope with the variety of illuminations in different images. These extracted features are essential for estimating the risk of developing diabetic retinopathy. The system has been tested on 1379 images from DIARETDB0, DIARETDB1, and MESSIDOR databases. A qualified optometrist verified that our system achieved 92% and 90.3% accuracy for blood vessel and hard exudates extraction respectively. The system takes only 0.6 seconds to analyze one image, and therefore it is suitable for real-time implementation in mobile devices.

4 - Nonlinear Optical Phase Conjugation

Adaptive Blind Signal and Image Processing