Showing papers on "Image gradient published in 2011"

FSIM: A Feature Similarity Index for Image Quality Assessment

Abstract: Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural similarity index brings IQA from pixel- to structure-based stage. In this paper, a novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS' perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics.

High quality depth map upsampling for 3D-TOF cameras

Abstract: This paper describes an application framework to perform high quality upsampling on depth maps captured from a low-resolution and noisy 3D time-of-flight (3D-ToF) camera that has been coupled with a high-resolution RGB camera. Our framework is inspired by recent work that uses nonlocal means filtering to regularize depth maps in order to maintain fine detail and structure. Our framework extends this regularization with an additional edge weighting scheme based on several image features based on the additional high-resolution RGB input. Quantitative and qualitative results show that our method outperforms existing approaches for 3D-ToF upsampling. We describe the complete process for this system, including device calibration, scene warping for input alignment, and even how the results can be further processed using simple user markup.

Gradient Profile Prior and Its Applications in Image Super-Resolution and Enhancement

Abstract: In this paper, we propose a novel generic image prior-gradient profile prior, which implies the prior knowledge of natural image gradients. In this prior, the image gradients are represented by gradient profiles, which are 1-D profiles of gradient magnitudes perpendicular to image structures. We model the gradient profiles by a parametric gradient profile model. Using this model, the prior knowledge of the gradient profiles are learned from a large collection of natural images, which are called gradient profile prior. Based on this prior, we propose a gradient field transformation to constrain the gradient fields of the high resolution image and the enhanced image when performing single image super-resolution and sharpness enhancement. With this simple but very effective approach, we are able to produce state-of-the-art results. The reconstructed high resolution images or the enhanced images are sharp while have rare ringing or jaggy artifacts.

The Canny Edge Detector Revisited

Abstract: Canny (IEEE Trans Pattern Anal Image Proc 8(6):679-698, 1986) suggested that an optimal edge detector should maximize both signal-to-noise ratio and localization, and he derived mathematical expressions for these criteria Based on these criteria, he claimed that the optimal step edge detector was similar to a derivative of a gaussian However, Canny's work suffers from two problems First, his derivation of localization criterion is incorrect Here we provide a more accurate localization criterion and derive the optimal detector from it Second, and more seriously, the Canny criteria yield an infinitely wide optimal edge detector The width of the optimal detector can however be limited by considering the effect of the neighbouring edges in the image If we do so, we find that the optimal step edge detector, according to the Canny criteria, is the derivative of an ISEF filter, proposed by Shen and Castan (Graph Models Image Proc 54:112---133, 1992) In addition, if we also consider detecting blurred (or non-sharp) gaussian edges of different widths, we find that the optimal blurred-edge detector is the above optimal step edge detector convolved with a gaussian This implies that edge detection must be performed at multiple scales to cover all the blur widths in the image We derive a simple scale selection procedure for edge detection, and demonstrate it in one and two dimensions

Multi-contrast reconstruction with Bayesian compressed sensing.

Abstract: Clinical imaging with structural MRI routinely relies on multiple acquisitions of the same region of interest under several different contrast preparations. This work presents a reconstruction algorithm based on Bayesian compressed sensing to jointly reconstruct a set of images from undersampled k-space data with higher fidelity than when the images are reconstructed either individually or jointly by a previously proposed algorithm, M-FOCUSS. The joint inference problem is formulated in a hierarchical Bayesian setting, wherein solving each of the inverse problems corresponds to finding the parameters (here, image gradient coefficients) associated with each of the images. The variance of image gradients across contrasts for a single volumetric spatial position is a single hyperparameter. All of the images from the same anatomical region, but with different contrast properties, contribute to the estimation of the hyperparameters, and once they are found, the k-space data belonging to each image are used independently to infer the image gradients. Thus, commonality of image spatial structure across contrasts is exploited without the problematic assumption of correlation across contrasts. Examples demonstrate improved reconstruction quality (up to a factor of 4 in root-mean-square error) compared to previous compressed sensing algorithms and show the benefit of joint inversion under a hierarchical Bayesian model.

A Quaternion Framework for Color Image Smoothing and Segmentation

Abstract: In this paper, we present feature/detail preserving models for color image smoothing and segmentation using the Hamiltonian quaternion framework. First, we introduce a novel quaternionic Gabor filter (QGF) which can combine the color channels and the orientations in the image plane. We show that these filters are optimally localized both in the spatial and frequency domains and provide a good approximation to quaternionic quadrature filters. Using the QGFs, we extract the local orientation information in the color images. Second, in order to model this derived orientation information, we propose continuous mixtures of appropriate exponential basis functions and derive analytic expressions for these models. These analytic expressions take the form of spatially varying kernels which, when convolved with a color image or the signed distance function of an evolving contour (placed in the color image), yield a detail preserving smoothing and segmentation, respectively. Several examples on widely used image databases are shown to depict the performance of our algorithms.

Fusion of infrared and visual images through region extraction by using multi scale center-surround top-hat transform

Abstract: Fusion of infrared and visual images is an important research area in image analysis. The purpose of infrared and visual image fusion is to combine the image information of the original images into the final fusion result. So, it is crucial to effectively extract the image information of the original images and reasonably combine them into the final fusion image. To achieve this purpose, an algorithm by using multi scale center-surround top-hat transform through region extraction is proposed in this paper. Firstly, multi scale center-surround top-hat transform is discussed and used to extract the multi scale bright and dim image regions of the original images. Secondly, the final extracted image regions for image fusion are constructed from the extracted multi scale bright and dim image regions. Finally, after a base image is calculated from the original images, the final extracted image regions are combined into the base image through a power strategy to form the final fusion result. Because the image information of the original images are well extracted and combined, the proposed algorithm is very effective for image fusion. Comparison experiments have been performed on different image sets, and the results verified the effectiveness of the proposed algorithm.

An improved Prewitt algorithm for edge detection based on noised image

Abstract: In this paper, an improved Prewitt algorithm for edge detection is proposed for the reason that the traditional Prewitt edge detection algorithm is sensitive to the noise. The traditional Prewitt edge detection operator only has two templates with horizontal and vertical directions. While the edge is in a plurality of directions, so operator with eight templates of different directions is put forward and it can detect more edges. In order to improve the capability of resisting noise, this paper put forward three improvements. First of all, the mean value rather than the maximum value of the gradient magnitude of the eight directions is used as the final gradient magnitude. Secondly, OTSU automatic threshold is used to set the gradient magnitude threshold. Again, an 8-neighborhood template is proposed to remove the isolated single pixel noise. The experimental results show that the improved algorithm improves the anti noise performance greatly, and detects the edges of the random noised image effectively.

A novel color image watermarking scheme in nonsampled contourlet-domain

Abstract: Geometric distortion is known as one of the most difficult attacks to resist, for it can desynchronize the location of the watermark and hence causes incorrect watermark detection. It is a challenging work to design a robust color image watermarking scheme against geometric distortions. Based on the support vector regression (SVR) and nonsubsampled contourlet transform (NSCT), we propose a new color image watermarking algorithm with good visual quality and reasonable resistance toward geometric distortions in this paper. Firstly, the geometrically invariant space is constructed by using color image normalization, and a significant region is obtained from the normalized color image by utilizing the invariant centroid theory. Then, the NSCT is performed on the green channel of the significant region. Finally, the digital watermark is embedded into host color image by modifying the low frequency NSCT coefficients, in which the HVS masking is used to control the watermark embedding strength. In watermark detection, according to the high correlation among different channels of the color image, the digital watermark can be recovered by using SVR technique. Experimental results show that the proposed color image watermarking is not only invisible and robust against common image processing operations such as filtering, noise adding, and JPEG compression etc., but also robust against the geometrical distortions.

Nonlinear transfer function-based local approach for color image enhancement

Abstract: The main objective of image enhancement is to improve some characteristic of an image to make it visually better one. This paper proposes a method for enhancing the color images based on nonlinear transfer function and pixel neighborhood by preserving details. In the proposed method, the image enhancement is applied only on the V (luminance value) component of the HSV color image and H and S component are kept unchanged to prevent the degradation of color balance between HSV components. The V channel is enhanced in two steps. First the V component image is divided into smaller overlapping blocks and for each pixel inside the block the luminance enhancement is carried out using nonlinear transfer function. In the second step, each pixel is further enhanced for the adjustment of the image contrast depending upon the center pixel value and its neighborhood pixel values. Finally, original H and S component image and enhanced V component image are converted back to RGB image. The subjective and objective performance evaluation shows that the proposed enhancement method yields better results without changing image original color in comparison with the conventional methods.

An Improved Canny Algorithm for Edge Detection

Abstract: Edge detection is an important part of digital image processing. This paper discusses the basic theory of edge detection, its method based on the traditional Canny operator, and proposes an improved algorithm based on the eight neighborhood gradient magnitude to overcome the disadvantages of being sensitive to noise in the calculation of the traditional canny operator gradient. The two thresholds of the traditional Canny operator need manual setting, so there are some defects to different images. This paper puts forward an adaptive threshold calculation by OTSU method. The experimental results prove that this improved method can effectively detect the edge of the image. And the continuity of the edge is strong, and positioning accuracy is high. .

Mathematical morphological edge detection for remote sensing images

Abstract: Edge detection is a terminology in image processing and computer vision particularly in the areas of feature detection and extraction to refer to the algorithms which aims at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities. The need of edge detection is to find the discontinuities in depth, discontinuities in surface orientation, changes in material properties and variations in scene illumination. Remote sensing images are generally corrupted from noise. Mathematical morphology is a new technique for edge detection. It is a theory and technique for analysis and processing of geometrical structures, based on set theory. Mathematical morphology was originally developed for binary images, and later extends to grey scale functions and images. Basically the noise can be easily suppressed by mathematical morphology. So by using mathematical morphology the image can be enhanced and the edges can be detected. The result of edge detection using mathematical morphology will be compared with Sobel edge detector, Prewitt edge detector, laplacian of gaussian edge detector and Canny edge detector.

Fast color-spatial feature based image retrieval methods

Abstract: In this paper, three types of image features are proposed to describe the color and spatial distributions of an image. In these features, the K-means algorithm is adopted to classify all of the pixels in an image into several clusters according to their colors. By measuring the spatial distance among the pixels in a same cluster, the three types of color spatial distribution (CSD) features of the image is obtained. Based on the three types of CSD features, three image retrieval methods are also provided. To accelerate the image retrieval methods, a fast filter is also presented to eliminate most undesired images in advance. A genetic algorithm is also given to decide the most suitable parameters which are used in the proposed image retrieval methods. The proposed image retrieval methods are simple. Moreover, the experiments show that the proposed methods can provide impressive results as well.

Image Magnification Using Interval Information

Abstract: In this paper, a simple and effective image-magnification algorithm based on intervals is proposed. A low-resolution image is magnified to form a high-resolution image using a block-expanding method. Our proposed method associates each pixel with an interval obtained by a weighted aggregation of the pixels in its neighborhood. From the interval and with a linear Kα operator, we obtain the magnified image. Experimental results show that our algorithm provides a magnified image with better quality (peak signal-to-noise ratio) than several existing methods.

A distributed Canny edge detector and its implementation on FPGA

Abstract: Edge detection is one of the key stages in image processing and object recognition. The Canny edge detector is one of the most widely-used edge detection algorithms due to its good performance. In this paper, we present a distributed Canny edge detection algorithm that results in significantly reduced memory requirements, decreased latency and increased throughput with no loss in edge detection performance as compared to the original Canny algorithm. The new algorithm uses a low-complexity 8-bin non-uniform gradient magnitude histogram to compute block-based hysteresis thresholds that are used by the Canny edge detector. Furthermore, an FPGA-based hardware architecture of our proposed algorithm is presented in this paper and the architecture is synthesized on the Xilinx Virtex-5 FPGA. Simulation results are presented to illustrate the performance of the proposed distributed Canny edge detector. The FPGA simulation results show that we can process a 512×512 image in 0.287ms at a clock rate of 100 MHz.

Hue-preserving color image enhancement using particle swarm optimization

Abstract: Image enhancement is aimed to improve image quality by maximizing the information content in the input image. In this article a PSO based hue preserving color image enhancement technique is proposed. The process is as follows. Image enhancement is considered as an optimization problem and particle swarm optimization (PSO) is used to solve it. The quality of the intensity image is improved by a parameterized transformation function, in which parameters are optimized by PSO based on an objective function. The intensity transformation function uses local and global information of the input image and the objective function considers the entropy and edge information to measure the image quality. The enhanced color image is then obtained by scaling, which sometimes leads to gamut problem for few pixels. Rescaling is done to the saturation component to remove the gamut problem. The algorithm is tested on several color images and results are compared with two other popular color image enhancement techniques like hue-preserving color image enhancement without gamut problem (HPCIE) and a genetic algorithm based approach to color image enhancement (GACIE). Visual analysis, detail and background variance of the resultant images are reported. It has been found that the proposed method produces better results compared to other two methods.

Content based Image Retrieval for Medical Images using Canny Edge Detection Algorithm

Abstract: The rapid expansion of digital data content has led to the need for rich descriptions and efficient Retrieval Tool. To develop this, content based image Retrieval method has played an important role in the field of image retrieval. This paper aims to provide an efficient medical image data Retrieval from a huge content of medical database using one of the images content such as image shape, because, efficient content-based image Retrieval in the medical domain is still a challenging problem. The main objective of this paper is to provide an efficient tool which is used for efficient medical image retrieval from a huge content of medical image database and which is used for further medical diagnosis purposes.

Geometrically Induced Force Interaction for Three-Dimensional Deformable Models

Abstract: In this paper, we propose a novel 3-D deformable model that is based upon a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions. This external force field is based upon hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so that topological changes are handled automatically. The relative geometrical configurations between the deformable model and the object boundaries contribute to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and it gives the deformable model a high invariancy in initialization configurations. The voxel interactions across the whole image domain provide a global view of the object boundary representation, giving the external force a long attraction range. The bidirectionality of the external force field allows the new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and broken boundaries. In addition, we show that by enhancing the geometrical interaction field with a nonlocal edge-preserving algorithm, the new deformable model can effectively overcome image noise. We provide a comparative study on the segmentation of various geometries with different topologies from both synthetic and real images, and show that the proposed method achieves significant improvements against existing image gradient techniques.

An edge detection approach of image fusion based on improved Sobel operator

Abstract: Anti-noise ability and edge continuity of Sobel edge detection algorithm are poor. In order to solve these problems, an improved method of Sobel operator is given in this paper. In addition, making use of fusion technology, a kind of method combined with improved Sobel operator, wavelet transform, Canny algorithm and Prewitt operator is put forward, which keeps their respective advantages. Experiments show that the fusion image effectively improves the accuracy of edge detection and gets a quite ideal edge detection effect.

Orientation-Matching Minimization for Image Denoising and Inpainting

Abstract: In this paper, we propose an orientation-matching functional minimization for image denoising and image inpainting. Following the two-step TV-Stokes algorithm (Rahman et al. in Scale space and variational methods in computer vision, pp. 473---482, Springer, Heidelberg, 2007; Tai et al. in Image processing based on partial differential equations, pp. 3---22, Springer, Heidelberg, 2006; Bertalmio et al. in Proc. conf. comp. vision pattern rec., pp. 355---362, 2001), a regularized tangential vector field with zero divergence condition is first obtained. Then a novel approach to reconstruct the image is proposed. Instead of finding an image that fits the regularized normal direction from the first step, we propose to minimize an orientation matching cost measuring the alignment between the image gradient and the regularized normal direction. This functional yields a new nonlinear partial differential equation (PDE) for reconstructing denoised and inpainted images. The equation has an adaptive diffusivity depending on the orientation of the regularized normal vector field, providing reconstructed images which have sharp edges and smooth regions. The additive operator splitting (AOS) scheme is used for discretizing Euler-Lagrange equations. We present the results of various numerical experiments that illustrate the improvements obtained with the new functional.

Color Image Segmentation using CIELab Color Space using Ant Colony Optimization

Abstract: segmentation plays vital role to understand an image. Only proper understanding of an image tells that what it represents and the various objects present in the image. In this paper we have proposed a new approach by using CIELab color space and Ant based clustering for the segmentation of color images. Image segmentation process divides an image into distinct regions with property that each region is characterized by unique feature such as intensity, color etc. This paper elaborates the ant based clustering for image segmentation. CMC distance is used to calculate the distance between pixels as this color metric gives good results with CIELab color space. Results shows the segmentation performed using ant based clustering and also shows that number of clusters for the image with particular CMC distance also varies. In order to evaluate the performance of proposed technique, MSE (Mean Square Error) is used. MSE is the global quality measure based on pixel difference. To verify our work, we have compared the results with results of color image quantization using LAB color model based on Bacteria Foraging Optimization (13).

Objective quality evaluation of visible and infrared color fusion image

Abstract: An evaluation for objectively assessing the quality of visible and infrared color fusion image is proposed. On the basis of the consideration that human perception is most sensitive to color, sharpness, and contrast when assessing the quality of color image, we propose four objective metrics: image sharpness metric (ISM), image contrast metric (ICM), color colorfulness metric (CCM), and color naturalness metric (CNM). The ISM is evaluated by image gradient information. The ICM is defined based on both gray and color histogram characteristics. A color chroma metric, as well as a color variety metric based on a color difference gradient, is proposed, respectively, to define the CCM. The CNM is defined by measuring the color distribution's similarity between the fusion image and nature image, which are of the same scene. All the color attributions are computed in the CIELAB color space. Experimental results show that the proposed objective metrics are meaningful and effective on color fusion image evaluation because they correspond well to subjective evaluation.

Principal component analysis of image gradient orientations for face recognition

Abstract: We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data population. We show that replacing intensities with gradient orientations and the l 2 norm with a cosine-based distance measure offers, to some extend, a remedy to this problem. Our scheme requires the eigen-decomposition of a covariance matrix and is as computationally efficient as standard l 2 intensity-based PCA. We demonstrate some of its favorable properties for the application of face recognition.

Image capturing apparatus and camera shake correction method, and computer-readable medium

Abstract: An image capturing apparatus includes: an image capturing unit capturing an image of an object; an image capture controller that causes the image capturing unit to capture first color component images having a first color component by multi-shot exposure, and causes the image capturing unit to capture second and third color component images, a displacement information acquiring unit that acquires displacement information; an image adding unit that aligns and adds the first color component images based on the displacement information to generate an added image; a calculator that calculates a first point spread function based on the displacement information; a first correcting unit that corrects the second and third color component images using the first point spread function; and a combining unit that combines the added image with the corrected second and third color component images.

QR code recognition based on image processing

Abstract: To solve the QR code recognition problem caused by ordinary camera collection, the recognition algorithm based on image processing is put forward in this paper. The whole process including image binarization, image tilt correction, image orientation, image geometric correction and image normalization allows images collected on different illumination conditions, different acquisition angles to be quickly identified. Based on other recognition algorithm, some improvements are presented in the image tilt correction, image orientation, image normalization and so on to speed up the image processing and to achieve more simply. Experiments show that the improved method can enhance the recognition speed of two-dimensional code and accuracy.

Morphological Edge Detection Algorithm Based on Multi-Structure Elements of Different Directions

Abstract: Edge detection is one of the important pre-processing steps in image analysis. Edges characterize boundaries and edge detection is one of the most difficult tasks in image processing hence it is a problem of fundamental importance in image processing. Edges in images are areas with strong intensity contrasts and a jump in intensity from one pixel to the next can create major variation in the picture quality. Edge detection of an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Conventionally, mathematical morphology edge detection methods use single and symmetrical structure elements. But they are difficult to detect complex edge feature, because they are only sensitive to image edge which has the same direction of structure elements. This paper proposed a novel edge detection algorithm based on multi-structure elements morphology of eight different directions. The eight different edge detection results are obtained by using morphological gradient algorithm respectively, and final edge results are obtained by using synthetic weighted method. The experimental results showed that the proposed algorithm is more efficient for edge detection than conventional mathematical morphological edge detection algorithms and differential edge detection operators.

Image processing unit, image processing method, and image processing program

Abstract: An image processing unit includes a memory unit in which continuously captured images including a reference image and a comparative image are stored, an image dividing unit to divide the reference image and the comparative image into image blocks of a predetermined size, a mean value calculator unit to calculate a mean value of pixel outputs in each image block of each of the reference and comparative images, a threshold determining unit to determine a threshold according to a mean value of pixel outputs of an image block of the reference image, and a determiner unit to compare the threshold with a difference value of the mean values of the pixel outputs in the image blocks of the reference and comparative images to be synthesized and determine whether the image blocks of the reference and comparative images are suitable for image synthesis based on a result of the comparison.