Image enhancement – An emergent pattern formation approach via decentralised multi-agent systems

Image restoration and enhancement are ones of fundamental problems in image processing. Many research activities, therefore, have been continuously carried out during almost three decades [1, 2, 3]. Inverse filtering, Wiener filtering with or without constrains, and Kalman filtering-smoothing techniques have been applied to the image restoration problem in the cases of either known or unknown blurring functions. Many methods in image restoration and enhancement rely upon the Fourier transformation and its discrete version; DFT (discrete Fourier transformation).

Digital image processing is versatile research in this era. Many researchers implement different types of organizations like image restoration, image enhancement, color image processing, image segmentation etc. Image enhancement technique is among the simplest and most appealing area of digital image processing. Enhancement techniques like brightness preservation, contrast enhancement highlight certain features means depend which part of the image want to be enhance some application some input image including noise, reduction or removal of noise is also form of image enhancement. Brightness preservation has enhanced visual quality of digital image so that the limitation contained in these images is used for various applications in a better way. A very popular technique for image enhancement is histogram equalization (HE) and curvelet transformation. HE technique is commonly employed for image enhancement because of its simplicity and comparatively better performance on almost all types of images. Another widely used technique is curvelet transformation. This technique is identified and separate bright regions of image but more error rate and low peak signal to noise ratio(PSNR), result of this technique is brightness preservation level is low and output image is gray. This paper design a hybrid model through discrete cosine transformation, discrete wavelet transformation and combine output of both techniques with image fusion. Proposed algorithm enhanced features and removal noise by decomposition of image using DWT and discrete cosine transformation, adaptive histogram equalization is very important part in this algorithm for smooth image. The tested results of different images are comparing with previous method, generating result with different parameters; less mean square error and high PSNR for improve the quality of an image. This paper presents a hybrid model used various parameter for enhance images like satellite images, medical images etc.

Phone cameras are currently used in various medical fields for processing color images. Today they can play an important role in facilitating access to some diagnostic services because they are available to many people, including doctors and patients who do not have access to advanced imaging systems. Image enhancement via cell phones is a highly desired preprocessing tool because many pictures and images are poor in quality. Processing of color images in one of the known color models, such as RGB, XYZ, or HSV15 is a very difficult task and has attracted much research interest in the last decade. In traditional color image processing, each color channel (red, green, and blue in the RGB color space, for example) is separately considered, then the color image is composed. In such a simple approach, the inherent correlation between the components are not taken into account; this fact results in color artifacts. For another approach that simultaneously processes the color components of the image,19 the most promising tool is the theory of quaternion algebra with 2D quaternion transforms, such as the Fourier, Hadamard, and cosine transforms. The 2D discrete Fourier transform (DFT) is still the primary transform used in image enhancement and filtration. Therefore, we will focus on an extension of this transform in quaternion arithmetic. The color image as a three-component image is mapped into pure quaternion data and can be called a color-in-quaternion image. This quaternion-transform-based approach can effectively be used in different applications of color science. First introduced by Hamilton,quaternion numbers now are used in bioinformatics, navigation systems,6 image and video processing, and in a number of studies on quaternions in color image processing. Here, we note that since quaternion multiplication is not commutative, different definitions of quaternion Fourier transforms can be used in color image processing.

Image processing technologies such as image enhancement generally utilize linear arithmetic operations to manipulate images. Recently, Jourlin and Pinoli successfully used the logarithmic image processing (LIP) model for several applications of image processing such as image enhancement and segmentation. In this paper, we introduce a parameterized LIP (PLIP) model that spans both the linear arithmetic and LIP operations and all scenarios in between within a single unified model. We also introduce both frequency- and spatial-domain PLIP-based image enhancement methods, including the PLIP Lee's algorithm, PLIP bihistogram equalization, and the PLIP alpha rooting. Computer simulations and comparisons demonstrate that the new PLIP model allows the user to obtain improved enhancement performance by changing only the PLIP parameters, to yield better image fusion results by utilizing the PLIP addition or image multiplication, to represent a larger span of cases than the LIP and linear arithmetic cases by changing parameters, and to utilize and illustrate the logarithmic exponential operation for image fusion and enhancement.

This paper addresses a novel and efficient algorithm for solving optimization problem in image processing applications. Image enhancement (IE) is one of the complex optimization problems in image processing. The main goal of this paper is to enhance color images such that the eminence of the image is more suitable than the original image from the perceptual viewpoint of human. Traditional methods require prior knowledge of the image to be enhanced, whereas the aim of the proposed biogeography based optimization (BBO) enhanced with blended migration operator (BMO) algorithm is to maximize the objective function in order to enhance the image contrast by maximizing the parameters like edge intensity, edge information, and entropy. Experimental results are compared with the current state-of-the-art approaches and indicate the superiority of the proposed technique in terms of subjective and objective evaluation.

Integrating machine vision in industrial automation involves providing computers typically of human vision. Vision engines are increasing in advances in processors like Digital Signal Processors (DSP), speed and decreasing in cost. The vision algorithms for creation of a vision application capable of selected image processing and analysis functions have been developed. They include storing an image into memory, the application of noise filters, segmenting the image and performing connected component analysis. The image processing is a computational intensive task with applications in various engineering fields.

This article aims to test FOS (first-order statistical) in extracting features of embryonated eggs. This test uses the initial step of image processing to get the best input image in feature extraction. The image processing method starts from the image acquisition process, then improves with image preprocessing and segmentation. Image acquisition in this study uses the concept of egg candling in a dark place captured with a smartphone camera. The acquisition results are improved by image preprocessing using gray scaling, image enhancement (by Histogram Equalization), and segmentation of chicken egg image. The segmentation results were extracted using FOS with five parameters: mean, entropy, variance, skewness, and kurtosis. Based on the calculation of these parameters, it is graphed and shows the difference in patterns between fertile and infertile eggs. However, some eggs have a similar pattern, thus affecting the identification process. The identification process used neural networks by the backpropagation method for training and testing. The training results provide an accuracy value of 100% of all training data; however, 80% of the new test data obtained test results at testing. This test is carried out with 100 data, 50 each for training and test data. Based on the test results, which significantly affect the level of accuracy is the feature extraction method. FOS pattern in detecting the fertility of chicken eggs by BP Neural Network is still categorized as low, so it is necessary to improve methods to get maximum results.

Quantum image processing represents a pivotal field in quantum remote sensing[1-3]. Dr. Siwen Bi and his research team pioneered the concept of quantum image data processing, having dedicated nearly 15 years to this technological advancement. This study focuses on developing core technologies for quantum-enhanced image processors, with particular emphasis on optimizing quantum enhancement algorithms, refining image processing methodologies, and implementing hardware solutions. The research aims to enhance both efficiency and quality in remote sensing and aerospace imaging applications[4-5], while providing efficient and precise image enhancement solutions for medical imaging, industrial inspection, remote sensing processing, and surveying fields. This study integrates quantum computing with traditional image processing technologies to develop quantum image enhancement algorithms, which have been applied to diverse imaging data types including remote sensing images, aerospace imagery, and medical imaging[6-8]. Through systematic research and experiments[9-11], we analyze key technologies in image enhancement processes such as quantum states, quantum superposition, quantum parallel computing, deep learning optimization, and hardware architecture design. Simultaneously, performance evaluations of various algorithms and hardware configurations were conducted using a combination of simulations and experimental methods. The research demonstrates that quantum image enhancement algorithms exhibit significant advantages over traditional methods in terms of processing accuracy, speed, and efficiency. Notably, image quality shows marked improvement during complex image processing. Furthermore, hardware-optimized quantum image processors can effectively support real-time processing of large-scale imaging data. As a cutting-edge image processing method, quantum image enhancement technology has demonstrated remarkable potential in remote sensing, aerospace, and medical fields[12-15]. Quantum computing offers innovative approaches to image processing, where the integration of deep learning and quantum computing can significantly enhance both efficiency and quality. With continuous advancements in quantum imaging hardware, this technology is poised to provide more efficient and precise solutions for image processing applications across various industries.

Fitting a causal dynamic model to an image is a fundamental problem in image processing, pattern recognition, and computer vision. In image restoration, for instance, the goal is to recover an estimate of the true image, preferably in the form of a parametric model, given an image that has been degraded by a combination of blur and additive white Gaussian noise. In texture analysis, on the other hand, a model of a particular texture image can serve as a tool for simulating texture patterns. Finally, in image enhancement one computes a model of the true image and the residuals between the image and the modeled image can be interpreted as the result of applying a de-noising filter. There are numerous other applications within the field of image processing that require a causal dynamic model. Such is the case in scene analysis, machined parts inspection, and biometric analysis, to name only a few. There are many types of causal dynamic models that have been proposed in the literature, among which the autoregressive moving average and state-space models (i.e., Kalman filter) are the most commonly used. In this paper we introduce a 2-D stochastic state-space system identification algorithm for fitting a quarter plane causal dynamic Roesser model to an image. The algorithm constructs a causal, recursive, and separable-in-denominator 2-D Kalman filter model. The algorithm is tested with three real images and the quality of the estimated images are assessed.

In image processing applications, one of the main preprocessing phases is image enhancement that is used to produce high quality image or enhanced image than the original input image. These enhanced images can be used in many applications such as remote sensing applications, geo-satellite images, etc. The quality of an image is affected due to several conditions such as by poor illumination, atmospheric condition, wrong lens aperture setting of the camera, noise, etc [2]. So, such degraded/low exposure images are needed to be enhanced by increasing the brightness as well as its contrast and this can be possible by the method of image enhancement. In this research work different image enhancement techniques are discussed and reviewed with their results. The aim of this study is to determine the application of deep learning approaches that have been used for image enhancement. Deep learning is a machine learning approach which is currently revolutionizing a number of disciplines including image processing and computer vision. This paper will attempt to apply deep learning to image filtering, specifically low-light image enhancement. The review given in this paper is quite efficient for future researchers to overcome problems that helps in designing efficient algorithm which enhances quality of the image.

Image enhancement methods were used to modify the images that were accepted as visual perception. Image enhancement methods mainly used for image contrast. Histogram compensation, used in the form of the cumulative distribution function (CDF), is one of the challenging factors in image processing. The goal of the enhancement is to improve the structural look of an image without impairing the input image. The enhancement techniques make it easier to identify key features by removing noise and other artifacts from an image. This article discusses studies to implement image enhancement in specific areas such as satellite imagery, infrared imagery, medical imagery and digital entertainment imagery, as well as information from the image enhancement method used. Keywords: Image enhancement, AHE, Histogram Equalization, CLAHE, spatial domain image, frequency domain method, etc. Cite this Article: Manish Sharma, Ashish Dubey, Prashant Badal. Review Paper on Image Enhancement using Various Methods. International Journal of Image Processing and Pattern Recognition. 2020; 6(1): 27–35p.

This article presents a new generation in parallel processing architecture for real-time image processing. The approach is implemented in a real time image processor chip, called the XiumTM-2, based on combining a fully associative array which provides the parallel engine with a serial RISC core on the same die. The architecture is fully programmable and can be programmed to implement a wide range of color image processing, computer vision and media processing functions in real time. The associative part of the chip is based on patented pending methodology of Associative Computing Ltd. (ACL), which condenses 2048 associative processors, each of 128 'intelligent' bits. Each bit can be a processing bit or a memory bit. At only 33 MHz and 0.6 micron manufacturing technology process, the chip has a computational power of 3 billion ALU operations per second and 66 billion string search operations per second. The fully programmable nature of the XiumTM-2 chip enables developers to use ACL tools to write their own proprietary algorithms combined with existing image processing and analysis functions from ACL's extended set of libraries.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Non-linear filters have proved to be powerful tools in image analysis, enhancement and restoration. Filters such as the ranked order, median, alpha-trimmed mean, weighted median and the class of morphological filters have been investigated thoroughly and have been shown to be indispensable in certain image processing applications. Another interesting, though unrelated, research area has been the use of fuzzy set theoretic operations in image processing. The underlying idea in employing the fuzzy set theory is to employ fuzzy membership grades to represent image characteristics and features. This paper seeks to unify these two methodologies resulting in an interesting approach to image enhancement problems. The emphasis is on using non-linear operations to generate a local fuzzy property plane. This is then used as an adaptive filter based on local image characteristics. In this paper, we propose a novel localized filtering approach similar to non- linear filters, the point of departure being that we work in the fuzzy property plane rather than the image domain itself. For instance, we may realize a fuzzy implementation based on the median filter which traditionally uses actual pixel intensities, to generate fuzzy memberships instead. In our experiments, we consider a locally adaptive contrast enhancement of x-ray images typically having low contrast. The results are compared with traditional enhancement techniques, such as histogram equalization. The results provide a better subjective quality as compared to other approaches as is also evident from the histogram distribution of the processed images.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In this paper the image processing method is used to enhance the pneumonia bacteria images. This paper recognized the bacteria images based on two domains. The enhancement techniques used for bacteria image enhancement were median filter, wiener filter, single scale retinex and multiscale retinex. Image enhancement has a very important role in digital image processing. The median and wiener filters were used for grayscale image enhancement. Then single scale retinex and multiscale retinex were used for color image enhancement. Based on performance metrics identified median filter is suitable for bacteria images in grayscale image enhancement and multiscale retinex is suitable for bacteria color image enhancement (Tab. 2, Fig. 8, Ref. 21).

Image enhancement, aimed at improving the image contrast and information quality, is one of the most critical steps in image processing. Due to insufficient enhancement and the mean shift problem of conventional image enhancement techniques, new artificial intelligence-based image enhancement approaches have become an inevitable need in image processing. This paper employs the krill herd algorithm (KHA) and particle swarm optimization (PSO) to suggest a novel hybrid approach, called (PSOKHA) for image enhancement. The suggested PSOKHA method is used in search of optimum transfer function parameters to increase the quality of the images. For comparative evaluation, the performance of the PSOKHA is compared with six latest successful enhancement methods: PSO, KHA, screened Poisson equation (SPE), histogram equalization (HE), brightness preserving dynamic fuzzy HE (BPDFHE), and adaptive gamma correction weighted distribution (AGCWD). Experiments results in testing images include a medical image, a satellite image, and a handwritten image, demonstrate that the suggested strategy can produce better enhanced images in terms of several measurement criteria such as contrast, PSNR, entropy, and structure similarity index (SSIM).