Search

Color constancy is the capability to observe the true color of a scene from its image regardless of the scene's illuminant. It is a significant part of the digital image processing pipeline and is utilized when the true color of an object is required. Most existing color constancy methods assume a uniform illuminant across the whole scene of the image, which is not always the case. Hence, their performances are influenced by the presence of multiple light sources. This paper presents a color constancy adjustment technique that uses the texture of the image pixels to select pixels with sufficient color variation to be used for image color correction. The proposed technique applies a histogram-based algorithm to determine the appropriate number of segments to efficiently split the image into its key color variation areas. The K-means++ algorithm is then used to divide the input image into the pre-determined number of segments. The proposed algorithm identifies pixels with sufficient color variation in each segment using the entropies of the pixels, which represent the segment's texture. Then, the algorithm calculates the initial color constancy adjustment factors for each segment by applying an existing statistics-based color constancy algorithm on the selected pixels. Finally, the proposed method computes color adjustment factors per pixel within the image by fusing the initial color adjustment factors of all segments, which are regulated by the Euclidian distances of each pixel from the centers of gravity of the segments. The experimental results on benchmark single- and multiple-illuminant image datasets show that the images that are obtained using the proposed algorithm have significantly higher subjective and very competitive objective qualities compared to those that are obtained with the state-of-the-art techniques.

Digital pathology has shown great importance for diagnostic purposes in the digital age by integrating basic image features into multi-modality information. We quantify the degree of correlation between the multiple texture features from H&E images and polarization parameter sets derived from Mueller matrix images of the same sample to provide more microstructural information for assisting diagnosis. The experimental result shows the correlations between texture feature and polarization parameter via Pearson coefficients. Polarization parameters t1, DL and the depolarization parameter Δ correlated with image texture features Tamura_Fcon and Tamura_Frgh, and can be used as powerful tools to quantitatively characterize cell nuclei related with tumor progression in breast pathological tissues. Polarization parameters δ and rL associated with the image texture feature Tamura_Flin have great potential for the quantitative characterization of proliferative fibers produced by inflammation. Furthermore, polarization parameters have the advantages of stable recognition in low resolution images. This work validates the associations between image texture features and polarization parameters and the merit of polarization imaging methods in low-resolution situations.

Blind noise-level estimation (NLE) is a fundamental issue in digital image processing. This paper provides a noise-level estimator for additive white Gaussian noise and multiplicative Gaussian noise using principal texture patches (PTPs). The proposed algorithm first identifies the principal texture of the noisy image by using the principal component analysis, and then, it chooses PTPs to calculate the noise level. The major contributions of this paper toward addressing the challenges in the NLE literature include: 1) analyzing the nonlinear relationship between the smallest eigenvalue, true noise level, number of chosen patches, and block size; 2) extracting PTPs; and 3) computing noise level using PTPs. The abundant experimental results show that the proposed method works well for various natural images over a large range of noise levels and it performs well for multiplicative noise. Compared with some state-of-the-art approaches, our method has the best performance and a faster running speed.

Since image processing aimed at reducing image noise can also remove important texture, standard methods for evaluating the capture and retention of image texture are currently being developed. Concurrently, the evolution of the intelligence and performance of camera noise-reduction (NR) algorithms poses a challenge for these protocols. Many NR algorithms are ‘content-aware’, which can lead to different levels of NR being applied to various regions within the same digital image. We review the requirements for improved texture measurement. The challenge is to evaluate image signal (texture) content without having a test signal interfere with the processing of the natural scene. We describe an approach to texture reproduction analysis that uses embedded periodic test signals within image texture regions. We describe a target that uses natural image texture combined with a multi-frequency periodic signal. This low-amplitude signal region is embedded in the texture image. Two approaches for embedding periodic test signals in image texture are described. The stacked sine-wave method uses a single combined, or stacked, region with several frequencycomponents. The second method uses a low-amplitude version of the IEC-61146-1 sine-wave multi-burst chart, combined with image texture. A 3x3 grid of smaller regions, each with a single frequency, constitutes the test target. Both methods were evaluated using a simulated digital camera capture-path that included detector noise and optical MTF, for a range of camera exposure/ISO settings. Two types of image texture were used with the method, natural grass and a computed ‘dead-leaves’ region composed of random circles. The embedded-signal methods tested for accuracy with respect to image noise over a wide range of levels, and then further in an evaluation of an adaptive noise-reduction image processing.

Medical image segmentation is a frequent processing step in image understanding and computer aided diagnosis. In this paper, we propose medical image texture segmentation using texture filter. Three different image enhancement technique s are utilized to remove strong speckle noise as well enhance the weak boundaries of medical images. We propose to exploit the concept of range filtering to extract the texture content of medical image. Experiment is conducted on ImageCLEF2010 database. Results show the efficacy of our proposed medical image texture segmentation.

This paper mainly discusses the applications of the fractional differential in image enhancement and its parallel processing optimization. Firstly, it makes the principle analysis of fractional calculus antagonistic properties and texture extraction of the digital image. Then it constructs the fractional differential mask and determines its operation rules. The simulation result shows that the mask enhances the digital images texture features effectively with the operation rules. As the mask processing costs large amount of calculation, which is time-consuming, therefore, the parallel algorithm of digital image processing is introduced, so that the large amount calculation problem is better resolved, and satisfactory results are obtained.

In digital image processing of remotely sensed data, texture analysis, filtering, and edge detection techniques, among others, may be improved through the use of variable window sizes which extend the analysis beyond the immediate pixel to a larger geographic area. In this paper, semivariograms are used to generate geographic windows, which are customized to the scale of observation. Three examples are used to illustrate the improvements over the use of arbitrarily selected fixed geometric windows in remote estimation of forest inventory, forest structure characteristics, and in land-cover classification. A program to handle the semivariance calculations is described. The code was written in the C programming language under AIX-Unix on an IBM RISC 6000 24-bit color workstation to support a common pixel-interleaved digital image format, and has been tested on optical and radar remote sensing imagery in three mapping studies.

A new technique for image texture analysis is described which uses the relative frequency of local extremes in grey level as the principal measure. This method is invariant to multiplicative gain changes (such as caused by changes in illumination level or film processing) and is invariant to image resolution and sampling rate if the image is not undersampled. The algorithm described is computationally simple and can be implemented in hardware for real-time analysis. Comparisons are made between this new method and the spatial dependence method of texture analysis using 49 samples of each of eight textures. The new method seems just as accurate and considerably faster.

Corrosion phenomena are usually difficult to recognize without a deep knowledge of the electrochemical properties of materials, this creates difficulties in the analysis of corrosion in practice, control measures are developed through image processing to solve these problems. This article describes a methodology for the analysis of the inner surface of steam pipelines by means of digital image processing (DIP). A classification algorithm is presented that is able to detect four levels of corrosion on the inner surface of steam pipelines. Gray-level co-occurrence matrix GLCM, which analyzes all the attributes of each image texture was employed. The performance of the classification algorithm was evaluated by using a confusion matrix, which showed very reliable classification results by comparing with experimental tests.

Computer-aided diagnosis (CAD) is a common tool for the detection of diseases, particularly different types of cancers, based on medical images. Digital image processing thus plays a significant role in the processing and analysis of medical images for diseases identification and detection purposes. In this study, an efficient CAD system for the acute lymphoblastic leukaemia (ALL) detection is proposed. The proposed approach entails two phases. In the first phase, the white blood cells (WBCs) are segmented from the microscopic blood image. The second phase involves extracting important features, such as shape and texture features from the segmented cells. Eventually, on the extracted features, Naive Bayes and k-nearest neighbour classifier techniques are implemented to identify the segmented cells into normal and abnormal cells. The performance of the proposed approach has been assessed through comprehensive experiments carried out on the well-known ALL-IDB data set of microscopic blood images. The experimental results demonstrate the superior performance of the proposed approach over the state-of-the-art in terms of accuracy rate in which achieved 98.7%.

Image processing is a computational process that transforms one or more input images into an output image. Image processing is frequently used to enhance an image for human viewing or interpretation, for example to improve contrast. Alternatively, and of more interest to robotics, it is the foundation for the process of feature extraction which will be discussed in much more detail in the next chapter.

Precision instrument, machinery manufacturing equipment needs to use a monitoring system to monitor production details in real time during its production and processing. However, the traditional remote monitoring system has a weak ability to process monitoring images, resulting in a low peak signal-to-noise ratio of the system. The surveillance image is blurry. Therefore, based on digital image processing, a new remote monitoring system for machinery manufacturing equipment is designed. In hardware, the front-end of voltage signal acquisition is redesigned, and the communication circuit is optimized. In software, digital image processing technology is used to sharpen the image fuzzy contour, correct the image texture, and enhance the fuzzy details of the monitoring image; the remote positioning and monitoring function of the system is set to realize the remote monitoring of mechanical manufacturing equipment. Experimental results: Compared with the monitoring system under the traditional design, the peak signal-to-noise ratio of the remote monitoring system designed this time is much higher than that of the traditional monitoring system. Visible digital image processing technology can help the surveillance system to enhance the fuzzy details of the image.

Power transformers are important assets in power grid. Winding deformation is one of main fault types of transformer internal failures. The accurate diagnosis of transformer winding deformation is significant and meaningful. In this study, an improved method of classifying winding deformation types is proposed. The polar plot is first plotted by using the amplitude and phase information of the measured frequency response analysis (FRA) traces, then the digital image processing technology is used to extract three image texture features from polar plot, and three support vector machines (SVMs) are independently trained by using the extracted texture features. As a result, a strong classifier is eventually obtained by combining the three trained SVMs, for fault type classification and recognition. The proposed method is verified on the experimental FRA data obtained from an actual model transformer, which demonstrates that the proposed method has more excellent performance compared with the traditional method based on the FRA trace and single SVM.

This study presents a novel feature extraction approach based on image processing algorithms for the automated detection of epileptic seizure activities in brain map representation of electroencephalography (EEG) signal using an efficient classification technique. The proposed technique uses independent component analysis to extract independent components (ICs) from the EEG signal and each extracted IC is transformed into an image termed as brain maps. Two feature extraction techniques namely closed neighbourhood gradient pattern (CNGP) and combined texture pattern (CTP) are propounded for automatic elimination of artefact brain maps. The extracted features are fed into the least square support vector machine (LSSVM) for automatic detection of epileptic brain maps. Extensive experimental result over the existing image processing techniques in literature demonstrates that the texture pattern representations of CNGP and CTP are improved to obtain better features to enhance the performance of texture classification. The obtained result shows that the LSSVM classifier with Gaussian RBF kernel is able to detect the epileptic brain map with a high accuracy rate. The results are reliable and it assists the neurologist to diagnose epileptic signals effortlessly by visually locating the brain area being affected by seizure activities.

There are different research and processing methods for Image Content Retrieval. This article is described the retrieval of remote sensing images method, which is based on the image texture features of the Fourier power spectrum. Firstly, a fixed size remote sensing image unit is selected from the remote sensing image, which is converted to a spectral image by FFT, and extract the eigenvalues in the frequency domain to constitute a searchable image eigenvalue database. Secondly, the similarity between the feature and the database features are examined by the Angular Cosine Algorithm. Lastly, similarity of the resulting image are sorted to select the best match. This method converts the remote sensing image into a spectral image, and makes the selection of the eigenvalue more simple. The Angle Cosine similarity detection method with weight is used to make the similarity detection more accurate.

[ES] La visión artificial es uno de los campos en mayor crecimiento en la actualidad que, junto con otras tecnologías como la Biometría o el Big Data, se ha convertido en el foco de interés de numerosas investigaciones y es considerada como una de las tecnologías del futuro. Este amplio campo abarca diversos métodos entre los que se encuentra el procesamiento y análisis de imágenes digitales. El éxito del análisis de imágenes y otras tareas de procesamiento de alto nivel, como pueden ser el reconocimiento de patrones o la visión 3D, dependerá en gran medida de la buena calidad de las imágenes de partida. Hoy en día existen multitud de factores que dañan las imágenes dificultando la obtención de imágenes de calidad óptima, esto ha convertido el (pre-) procesamiento digital de imágenes en un paso fundamental previo a la aplicación de cualquier otra tarea de procesado. Los factores más comunes son el ruido y las malas condiciones de adquisición: los artefactos provocados por el ruido dificultan la interpretación adecuada de la imagen y la adquisición en condiciones de iluminación o exposición deficientes, como escenas dinámicas, causan pérdida de información de la imagen que puede ser clave para ciertas tareas de procesamiento. Los pasos de (pre-)procesamiento de imágenes conocidos como suavizado y realce se aplican comúnmente para solventar estos problemas: El suavizado tiene por objeto reducir el ruido mientras que el realce se centra en mejorar o recuperar la información imprecisa o dañada. Con estos métodos conseguimos reparar información de los detalles y bordes de la imagen con una nitidez insuficiente o un contenido borroso que impide el (post-)procesamiento óptimo de la imagen. Existen numerosos métodos que suavizan el ruido de una imagen, sin embargo, en muchos casos el proceso de filtrado provoca emborronamiento en los bordes y detalles de la imagen. De igual manera podemos encontrar una enorme cantidad de técnicas de realce que intentan combatir las pérdidas de información, sin embargo, estas técnicas no contemplan la existencia de ruido en la imagen que procesan: ante una imagen ruidosa, cualquier técnica de realce provocará también un aumento del ruido. Aunque la idea intuitiva para solucionar este último caso será el previo filtrado y posterior realce, este enfoque ha demostrado no ser óptimo: el filtrado podrá eliminar información que, a su vez, podría no ser recuperable en el siguiente paso de realce. En la presente tesis doctoral se propone un modelo basado en teoría de grafos para el procesamiento de imágenes en color. En este modelo, se construye un grafo para cada píxel de tal manera que sus propiedades permiten caracterizar y clasificar dicho pixel. Como veremos, el modelo propuesto es robusto y capaz de adaptarse a una gran variedad de aplicaciones. En particular, aplicamos el modelo para crear nuevas soluciones a los dos problemas fundamentales del procesamiento de imágenes: suavizado y realce. Se ha estudiado el modelo en profundidad en función del umbral, parámetro clave que asegura la correcta clasificación de los píxeles de la imagen. Además, también se han estudiado las posibles características y posibilidades del modelo que nos han permitido sacarle el máximo partido en cada una de las posibles aplicaciones. Basado en este modelo se ha diseñado un filtro adaptativo capaz de eliminar ruido gaussiano de una imagen sin difuminar los bordes ni perder información de los detalles. Además, también ha permitido desarrollar un método capaz de realzar los bordes y detalles de una imagen al mismo tiempo que se suaviza el ruido presente en la misma. Esta aplicación simultánea consigue combinar dos operaciones opuestas por definición y superar así los inconvenientes presentados por el enfoque en dos etapas., [CA] La visió artificial és un dels camps en major creixement en l'actualitat que, junt amb altres tecnlogies com la Biometria o el Big Data, s'ha convertit en el focus d'interés de nombroses investigacions i és considerada com una de les tecnologies del futur. Aquest ampli camp comprén diversos m`etodes entre els quals es troba el processament digital d'imatges i anàlisis d'imatges digitals. L'èxit de l'anàlisis d'imatges i altres tasques de processament d'alt nivell, com poden ser el reconeixement de patrons o la visió 3D, dependrà en gran manera de la bona qualitat de les imatges de partida. Avui dia existeixen multitud de factors que danyen les imatges dificultant l'obtenció d'imatges de qualitat òptima, açò ha convertit el (pre-) processament digital d'imatges en un pas fonamental previa la l'aplicació de qualsevol altra tasca de processament. Els factors més comuns són el soroll i les males condicions d'adquisició: els artefactes provocats pel soroll dificulten la inter- pretació adequada de la imatge i l'adquisició en condicions d'il·luminació o exposició deficients, com a escenes dinàmiques, causen pèrdua d'informació de la imatge que pot ser clau per a certes tasques de processament. Els passos de (pre-) processament d'imatges coneguts com suavitzat i realç s'apliquen comunament per a resoldre aquests problemes: El suavitzat té com a objecte reduir el soroll mentres que el real se centra a millorar o recuperar la informació imprecisa o danyada. Amb aquests mètodes aconseguim reparar informació dels detalls i bords de la imatge amb una nitidesa insuficient o un contingut borrós que impedeix el (post-)processament òptim de la imatge. Existeixen nombrosos mètodes que suavitzen el soroll d'una imatge, no obstant això, en molts casos el procés de filtrat provoca emborronamiento en els bords i detalls de la imatge. De la mateixa manera podem trobar una enorme quantitat de tècniques de realç que intenten combatre les pèrdues d'informació, no obstant això, aquestes tècniques no contemplen l'existència de soroll en la imatge que processen: davant d'una image sorollosa, qualsevol tècnica de realç provocarà també un augment del soroll. Encara que la idea intuïtiva per a solucionar aquest últim cas seria el previ filtrat i posterior realç, aquest enfocament ha demostrat no ser òptim: el filtrat podria eliminar informació que, al seu torn, podria no ser recuperable en el seguënt pas de realç. En la present Tesi doctoral es proposa un model basat en teoria de grafs per al processament d'imatges en color. En aquest model, es construïx un graf per a cada píxel de tal manera que les seues propietats permeten caracteritzar i classificar el píxel en quëstió. Com veurem, el model proposat és robust i capaç d'adaptar-se a una gran varietat d'aplicacions. En particular, apliquem el model per a crear noves solucions als dos problemes fonamentals del processament d'imatges: suavitzat i realç. S'ha estudiat el model en profunditat en funció del llindar, paràmetre clau que assegura la correcta classificació dels píxels de la imatge. A més, també s'han estudiat les possibles característiques i possibilitats del model que ens han permés traure-li el màxim partit en cadascuna de les possibles aplicacions. Basat en aquest model s'ha dissenyat un filtre adaptatiu capaç d'eliminar soroll gaussià d'una imatge sense difuminar els bords ni perdre informació dels detalls. A més, també ha permés desenvolupar un mètode capaç de realçar els bords i detalls d'una imatge al mateix temps que se suavitza el soroll present en la mateixa. Aquesta aplicació simultània aconseguix combinar dues operacions oposades per definició i superar així els inconvenients presentats per l'enfocament en dues etapes., [EN] Computer vision is one of the fastest growing fields at present which, along with other technologies such as Biometrics or Big Data, has become the focus of interest of many research projects and it is considered one of the technologies of the future. This broad field includes a plethora of digital image processing and analysis tasks. To guarantee the success of image analysis and other high-level processing tasks as 3D imaging or pattern recognition, it is critical to improve the quality of the raw images acquired. Nowadays all images are affected by different factors that hinder the achievement of optimal image quality, making digital image processing a fundamental step prior to the application of any other practical application. The most common of these factors are noise and poor acquisition conditions: noise artefacts hamper proper image interpretation of the image; and acquisition in poor lighting or exposure conditions, such as dynamic scenes, causes loss of image information that can be key for certain processing tasks. Image (pre-) processing steps known as smoothing and sharpening are commonly applied to overcome these inconveniences: Smoothing is aimed at reducing noise and sharpening at improving or recovering imprecise or damaged information of image details and edges with insufficient sharpness or blurred content that prevents optimal image (post-)processing. There are many methods for smoothing the noise in an image, however in many cases the filtering process causes blurring at the edges and details of the image. Besides, there are also many sharpening techniques, which try to combat the loss of information due to blurring of image texture and need to contemplate the existence of noise in the image they process. When dealing with a noisy image, any sharpening technique may amplify the noise. Although the intuitive idea to solve this last case would be the previous filtering and later sharpening, this approach has proved not to be optimal: the filtering could remove information that, in turn, may not be recoverable in the later sharpening step. In the present PhD dissertation we propose a model based on graph theory for color image processing from a vector approach. In this model, a graph is built for each pixel in such a way that its features allow to characterize and classify the pixel. As we will show, the model we proposed is robust and versatile: potentially able to adapt to a variety of applications. In particular, we apply the model to create new solutions for the two fundamentals problems in image processing: smoothing and sharpening. To approach high performance image smoothing we use the proposed model to determine if a pixel belongs to a at region or not, taking into account the need to achieve a high-precision classification even in the presence of noise. Thus, we build an adaptive soft-switching filter by employing the pixel classification to combine the outputs from a filter with high smoothing capability and a softer one to smooth edge/detail regions. Further, another application of our model allows to use pixels characterization to successfully perform a simultaneous smoothing and sharpening of color images. In this way, we address one of the classical challenges within the image processing field. We compare all the image processing techniques proposed with other state-of-the-art methods to show that they are competitive both from an objective (numerical) and visual evaluation point of view.

This paper presents an investigation of image texture approaches for mapping sub-pixel fluvial grain-size features from airborne imagery, allowing for the rapid acquisition of surface sand and coarse fraction (>1.41 mm) grain-size information. Imagery at 30 mm resolution was acquired over four gravel bars from the Fraser River (British Columbia, Canada). Combined first-order and second-order image texture approaches (windowed standard deviation filter and the grey level co-occurrence matrix) were used. First-order image texture, through the application of a standard deviation filter and subsequent thresholding was used to detect the presence of surface sand, with optimal accuracy achieved at 91 ± 1.9%. A wide-ranging parameter space investigation was used to derive optimum parameters for the grey level co-occurrence matrix. Subsequently first- and second-order image textures were used in multiple linear regression to achieve good calibrations with several sub-pixel grain-size percentiles; relative error at 1.44%, 3.18%, 6.80% and 10.6% for D5, D16, D35 and D50, respectively. The larger percentiles of D84 and D95 had relative errors of 24.7% and 29.7%, respectively. The breakdown of calibration precision for larger percentiles is attributed to a ‘pixel averaging effect’. It is concluded that multispectral imagery is not required, because sufficient image texture information can be derived from standard colour imagery. Recommendations are suggested for application of this method to other localities and datasets, thus reducing exhaustive parameter searches in future studies.

Image segmentation is an elemental step in the process of extraction of some useful information from digital images. It tends to divide the image into various regions, the regions may be entities of the real world contained in the scene whose image is being considered. The main objective of segmentation is to make the representation of an image simpler and much easier to evaluate. Image segmentation finds its practical applications in the field of medical imaging and diagnosis, object recognition and detection tasks in the field of artificial intelligence etc. A wide class of problems in segmentation can be accomplished using lower processing levels; however in case of complex scenes, a simple general approach may not lead to correct and complete segmentation, so a means of higher processing levels that use certain specific knowledge of the problem domain is necessary. There is no generally accepted segmentation algorithm that can work for all types of images, a wide range of segmentation algorithms are available that are combined with all necessary information regarding the particular problem area to solve the problem. Any segmentation algorithm can be chosen according to its suitability for the problem in hand.

Propose a novel 3D image registration method.Does NOT impose any parametric form on the underlying geometric transformation.The underlying geometric transformation is even allowed to change over location.The new method performs much better than the existing methods. In many imaging applications, including image fusion and image sequence monitoring, we need to geometrically match up one image to another of a same scene, so that information from different images can be compared or combined properly. This is the image registration (IR) problem that has received much attention in recent years due to its broad applications. In the literature, early IR methods are for analyzing 2D images. Because of the rapid progress in image acquisition technologies, 3D images have become increasingly popular in magnetic resonance imaging (MRI) and other applications in recent years. Since the structure of a typical 3D image is substantially more complicated than that of a typical 2D image, 3D image registration is challenging. In this paper, we develop a new 3D image registration method using local smoothing statistical methods. By the flexibility of local smoothing, our method does not require any parametric form or other global regularity conditions on the related geometric transformation. It is shown that this method works well in practice.

Multisensor image fusion technologies, which convey image information from different sensor modalities to a single image, have been a growing interest in recent research. In this paper, we propose a novel multisensor image fusion method based on multiple visual features measurement with gradient domain guided filtering. First, a Gaussian smoothing filter is employed to decompose each source image into two components: approximate component formed by homogeneous regions and detail component with sharp edges. Second, an effective decision map construction model is presented by measuring three key visual features of the input sensor image: contrast saliency, sharpness, and structure saliency. Third, a gradient domain guided filtering-based decision map optimization technique is proposed to make full use of spatial consistency and generate weight maps. Finally, the resultant image is fused with the weight maps and then is experimentally verified through multifocus image, multimodal medical image, and infrared-visible image fusion. The experimental results demonstrate that the proposed method can achieve better performance than state-of-the-art methods in terms of subjective visual effect and objective evaluation.

Example-based super-resolution algorithms, which predict unknown high-resolution image information using a relationship model learnt from known high- and low-resolution image pairs, have attracted considerable interest in the field of image processing. In this paper, we propose a multi-example feature-constrained back-projection method for image super-resolution. Firstly, we take advantage of a feature-constrained polynomial interpolation method to enlarge the low-resolution image. Next, we consider low-frequency images of different resolutions to provide an example pair. Then, we use adaptive kNN search to find similar patches in the low-resolution image for every image patch in the high-resolution low-frequency image, leading to a regression model between similar patches to be learnt. The learnt model is applied to the low-resolution high-frequency image to produce high-resolution high-frequency information. An iterative back-projection algorithm is used as the final step to determine the final high-resolution image. Experimental results demonstrate that our method improves the visual quality of the high-resolution image.

Image retrieval means to recover the original image from the reconstructed image, here in this paper we have discussed latest techniques in the field of image retrieval for image processing. Content Based Image Retrieval (CBIR) is one of the most exciting and fastest growing research areas in the field of Image Processing. The techniques presented are Boosting image retrieval, soft query in image retrieval system, content based image retrieval by integration of metadata encoded multimedia features, and object based image retrieval and Bayesian image retrieval system. Some probable future research directions are also presented here to explore research area in the field of image retrieval

Image quality assessment is very crucial for certain image processing applications. Images can be distorted from multiple sources like a fault in sensors, camera shake, poor lighting, over-exposure etc. All these distortions reduce the visual quality of images. Assessing the quality of images can be done with the use of a reference image of the same scene or without it. Without the use of reference image, quality assessment is a very difficult task. Machine learning approaches are very common in no-reference image quality assessment. No reference strategies are very useful if the type of distortion is known. Contrast assessment is a very important application in image processing as poor contrast images are difficult for automated image processing. In this paper, we propose a no-reference image quality measure for images with respect to contrast using random forest regression and validate the results using standard datasets. Experimental results on standard datasets show that the proposed method demonstrates promising results when compared to existing no-reference techniques and the proposed method shows high correlation values with human opinion scores.

Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles, which reduces the contrast, changes the color, and makes the object features difficult to identify by human vision and by some outdoor computer vision systems. Therefore image dehazing is an important issue and has been widely researched in the field of computer vision. The role of image dehazing is to remove the influence of weather factors in order to improve the visual effects of the image and provide benefit to post-processing. This paper reviews the main techniques of image dehazing that have been developed over the past decade. Firstly, we innovatively divide a number of approaches into three categories: image enhancement based methods, image fusion based methods and image restoration based methods. All methods are analyzed and corresponding sub-categories are introduced according to principles and characteristics. Various quality evaluation methods are then described, sorted and discussed in detail. Finally, research progress is summarized and future research directions are suggested.

age Segmentation is an essential procedure of image processing as well as knowing it. Mainly it's known due to the method of splitting the image into various elements of homogeneity. The goal of image segmentation is to make simpler presentation of an image into incredible which is meaningful as well as easy to understand. It is mainly utilized to know the location of objects, boundaries, lines etc in the digital images. Clustering technique is a method which shows the data set or pixels are replaced by cluster, pixels might be together because of the same color, texture etc. This paper represents the effect of mixed regions on image segmentation and color images contain maximum information for efficient image segmentation. KeywordsProcessing,Image Segmentation Techniques

The Human Visual System (HVS) model based image quality metrics, correlates strongly with the evaluations of image quality as well as with human observer performance in the visual recognition process. Physiological modeling of retina plays a vital role in the development of high-performance image processing methods for better visual perception. For image processing in medical diagnosis, one has to follow several steps like image preprocessing, image segmentation, feature extraction, image recognition, and interpretation. This work aims at developing human visual system based image processing which stands advantageous when compared with the conventional processing methods. The main aim of this work is to develop a model for retina, which has complex neural structure, capable of detecting the incoming light signal and transforms the signal before transmitting it through the optic nerve. This retinal model comprises of the photoreceptor, outer-plexiform and inner-plexiform layers exhibiting the properties of compression and spatiotemporal filtering in the processing of visual information. The spatial frequency value is evaluated using Discrete Cosine Transform (DCT) technique thereby enhancing the contrast visibility in the dark area and maintaining the same in the bright area using photoreceptor layer of the retina. Contour contrast enhancement is achieved by modeling outer- plexiform layer of retina and parvo channel of the inner-plexiform layer is modeled to extract finer details of the image. The properties like luminance, spatial and temporal frequencies were considered to develop the human visual system based retinal model. The proposed model is applied to a wide variety of medical images and with simulated results it has been proved that the texture feature values of the processed image are found to be higher than the original input image. Further, this method proves to be more flexible which enables easier practical implementation when compared to that of generic medical image processing techniques.

Underwater images suffer from less visibility problem due to principal of light refraction under water environment. Therefore, this paper address the problem of enhancing the contrast of the underwater images captured under different lightning conditions and depths. This paper presenting the comparison of the two distinct popular enhancement methods and it concluded that this different method generates two different image sets from original low contrast image. These sets can be further utilized for object segmentation using fuzzy based clustering methods efficiently. Paper presents enhancement using contrast limit adaptive histogram equalization (CLAHE) and, global contrast adjustment method.. The CLAHE method is used to the images so that contrast of output images is improved also gives better information. It is concluded that using the different contrast enhancement method different image data set can be created for same input image which can be utilized for higher level processing task and features extractions. General Terms Image processing, Underwater Images

Utilization of online medical images was limited due to the lack of effective search methods, and text-based searches have been a dominating approach for the medical database management. Medical images of various modalities cannot be effectively indexed or organized with traditional text-based retrieval techniques. This has led to the use of image content for the processing and organizing the database, constituting the so-called content-based image retrieval systems. This paper presents two proposed methods, namely fuzzy connectedness image segmentation with geometric moments (FCISGMs), and localized entropy-based medical image retrieval (LEBIR) for retrieval of Digital Imaging and Communications in Medicine images. FCISGM exploits shape features for precise image retrieval by using fuzzy connectedness image segmentation. LEBIR uses localized entropy for minimizing number of computation which results in efficient image retrieval. Experimental evaluation reveals that the proposed methods outperform...

The 6-degrees of freedom (DOF) image localization, which aims to calculate the spatial position and rotation of a camera, is a challenging problem for most location-based services. In existing approaches, this problem is often tackled by finding the matches between 2D image points and 3D structure points so as to derive the location information via direct linear transformation algorithm. However, as these 2D-to-3D-based approaches need to reconstruct the 3D structure points of the scene, they may not be flexible enough to employ massive and increasing geo-tagged data. To this end, this paper presents a novel approach for 6-DOF image localization by fusing candidate poses relative to reference images. In this approach, we propose to localize an input image according to the position and rotation information of multiple geo-tagged images retrieved from a reference dataset. From the reference images, an efficient relative pose estimation algorithm is proposed to derive a set of candidate poses for the input image. Each candidate pose encodes the relative rotation and direction of the input image with respect to a specific reference image. Finally, these candidate poses can be fused together by minimizing a well-defined geometry error so that the 6-DOF location of the input image is effectively derived. Experimental results show that our method can obtain satisfactory localization accuracy. In addition, the proposed relative pose estimation algorithm is much faster than existing work.

Previous works on image completion typically aim to produce visually plausible results rather than factually correct ones. In this paper, we propose an approach to faithfully complete the missing regions of an image. We assume that the input image is taken at a well-known landmark, so similar images taken at the same location can be easily found on the Internet. We first download thousands of images from the Internet using a text label provided by the user. Next, we apply two-step filtering to reduce them to a small set of candidate images for use as source images for completion. For each candidate image, a co-matching algorithm is used to find correspondences of both points and lines between the candidate image and the input image. These are used to find an optimal warp relating the two images. A completion result is obtained by blending the warped candidate image into the missing region of the input image. The completion results are ranked according to combination score, which considers both warping and blending energy, and the highest ranked ones are shown to the user. Experiments and results demonstrate that our method can faithfully complete images.

Image Segmentations play a heavy role in areas such as computer vision and image processing due to its broad usage and immense applications. Because of the large importance of image segmentation a number of algorithms have been proposed and different approaches have been adopted. Segmentation divides an image into distinct regions containing each pixel with similar attributes. The objective of apportioning is to simplify and/or alter the representation of an image into something that is more meaningful and more comfortable to break down. This paper discusses the various techniques implemented for image segmentation and discusses the various Computations that can be performed on the graphics processing unit (GPU) by means of the CUDA architecture in order to achieve fast performance and increase the utilization of available system resources. A REVIEW ON IMAGE SEGMENTATION USING GPU

How do we evaluate the quality of an image and determine which variation of an image a human observer would find most visually pleasing without using a human in the loop? The answer lies in utilizing image quality measures. These measures have an abundance of uses in instrumentation and measurement applications [1], [2] and can be categorized into three groups: full-reference [3], reduced- reference, and no-reference [4]. The full-reference and reduced-reference are not suitable for real applications, since an optimal original reference image does not always exist [5]. For instance, consider taking a picture with your cell phone in poor lighting or bright sunshine. These are examples of resulting images that are considered to suffer from distortions. The distorted image is the only image available for processing, and thus, we have no-reference of what a good distortion free image should look like. Hence, using a no-reference image quality measure is desirable.

Image merging is a process of obtaining one image from multiple. The resulting image carries more information about the photographed scene, than each of the originals. Such an image can be more useful when we deal with human or image processing system. Algorithms that performed this task are used in a wide applying in practical: computer vision, robotics, medicine, forensics, etc. The most popular image focus assessment for usage in the task of forming a multi-focused image were considered. The classification of metrics evaluation was done. Experiments on suggested image focus metrics construction were minutely described. The forms of referenced characteristics were proposed. Using correlation analysis to select the best from the point of view of the problem, metrics and estimated time required for their computation were discussed.