Showing papers on "Image scaling published in 2011"

Discrete Wavelet Transform-Based Satellite Image Resolution Enhancement

Abstract: Satellite images are being used in many fields of research. One of the major issues of these types of images is their resolution. In this paper, we propose a new satellite image resolution enhancement technique based on the interpolation of the high-frequency subbands obtained by discrete wavelet transform (DWT) and the input image. The proposed resolution enhancement technique uses DWT to decompose the input image into different subbands. Then, the high-frequency subband images and the input low-resolution image have been interpolated, followed by combining all these images to generate a new resolution-enhanced image by using inverse DWT. In order to achieve a sharper image, an intermediate stage for estimating the high-frequency subbands has been proposed. The proposed technique has been tested on satellite benchmark images. The quantitative (peak signal-to-noise ratio and root mean square error) and visual results show the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.

Real-Time Artifact-Free Image Upscaling

Abstract: The problem of creating artifact-free upscaled images appearing sharp and natural to the human observer is probably more interesting and less trivial than it may appear. The solution to the problem, often referred to also as “single-image super-resolution,” is related both to the statistical relationship between low-resolution and high-resolution image sampling and to the human perception of image quality. In many practical applications, simple linear or cubic interpolation algorithms are applied for this task, but the results obtained are not really satisfactory, being affected by relevant artifacts like blurring and jaggies. Several methods have been proposed to obtain better results, involving simple heuristics, edge modeling, or statistical learning. The most powerful ones, however, present a high computational complexity and are not suitable for real-time applications, while fast methods, even if edge adaptive, are not able to provide artifacts-free images. In this paper, we describe a new upscaling method (iterative curvature-based interpolation) based on a two-step grid filling and an iterative correction of the interpolated pixels obtained by minimizing an objective function depending on the second-order directional derivatives of the image intensity. We show that the constraints used to derive the function are related with those applied in another well-known interpolation method, providing good results but computationally heavy (i.e., new edge-directed interpolation (NEDI). The high quality of the images enlarged with the new method is demonstrated with objective and subjective tests, while the computation time is reduced of one to two orders of magnitude with respect to NEDI so that we were able, using a graphics processing unit implementation based on the nVidia Compute Unified Device Architecture technology, to obtain real-time performances.

Image Interpolation Via Regularized Local Linear Regression

Abstract: The linear regression model is a very attractive tool to design effective image interpolation schemes. Some regression-based image interpolation algorithms have been proposed in the literature, in which the objective functions are optimized by ordinary least squares (OLS). However, it is shown that interpolation with OLS may have some undesirable properties from a robustness point of view: even small amounts of outliers can dramatically affect the estimates. To address these issues, in this paper we propose a novel image interpolation algorithm based on regularized local linear regression (RLLR). Starting with the linear regression model where we replace the OLS error norm with the moving least squares (MLS) error norm leads to a robust estimator of local image structure. To keep the solution stable and avoid overfitting, we incorporate the l2-norm as the estimator complexity penalty. Moreover, motivated by recent progress on manifold-based semi-supervised learning, we explicitly consider the intrinsic manifold structure by making use of both measured and unmeasured data points. Specifically, our framework incorporates the geometric structure of the marginal probability distribution induced by unmeasured samples as an additional local smoothness preserving constraint. The optimal model parameters can be obtained with a closed-form solution by solving a convex optimization problem. Experimental results on benchmark test images demonstrate that the proposed method achieves very competitive performance with the state-of-the-art interpolation algorithms, especially in image edge structure preservation.

Perception-motivated interpolation of image sequences

Abstract: We present a method for image interpolation that is able to create high-quality, perceptually convincing transitions between recorded images. By implementing concepts derived from human vision, the problem of a physically correct image interpolation is relaxed to that of image interpolation which is perceived as visually correct by human observers. We find that it suffices to focus on exact edge correspondences, homogeneous regions and coherent motion to compute convincing results. A user study confirms the visual quality of the proposed image interpolation approach. We show how each aspect of our approach increases perceived quality of the result. We compare the results to other methods and assess achievable quality for different types of scenes.

Curvature Interpolation Method for Image Zooming

Abstract: We introduce a novel image zooming algorithm, called the curvature interpolation method (CIM), which is partial-differential-equation (PDE)-based and easy to implement. In order to minimize artifacts arising in image interpolation such as image blur and the checkerboard effect, the CIM first evaluates the curvature of the low-resolution image. After interpolating the curvature to the high-resolution image domain, the CIM constructs the high-resolution image by solving a linearized curvature equation, incorporating the interpolated curvature as an explicit driving force. It has been numerically verified that the new zooming method can produce clear images of sharp edges which are already denoised and superior to those obtained from linear methods and PDE-based methods of no curvature information. Various results are given to prove effectiveness and reliability of the new method.

State-of-the-Art Medical Image Registration Methodologies: A Survey

Abstract: Almost all computer vision applications, from remote sensing and cartography to medical imaging and biometrics, use image registration or alignment techniques that establish spatial correspondence (one-to-one mapping) between two or more images. These images depict either one planar (2-D) or volumetric (3-D) scene or several such scenes and can be taken at different times, from various viewpoints, and/or by multiple sensors. In medical image processing and analysis, the image registration is instrumental for clinical diagnosis and therapy planning, e.g., to follow disease progression and/or response to treatment, or integrate information from different sources/modalities to form more detailed descriptions of anatomical objects-of-interest. The unified registration goal – aligning a 2-D or 3-D target (sensed) image with a reference image – is reached by specifying a mathematical model of image transformations for and determining model parameters of the desired alignment. Frequently, the parameters provide an optimum of a goal function supported by the parameter space, so that the registration reduces to a certain optimization problem. This chapter overviews the 2-D and the 3-D medical image registration with special reference to the state-of-the-art robust techniques proposed for the last decade and discusses their advantages, drawbacks, and practical implementations.

Ant colony optimization for wavelet-based image interpolation using a three-component exponential mixture model

Abstract: Wavelet-based image interpolation typically treats the input image as the low frequency subbands of an unknown wavelet-transformed high-resolution image, and then produces the unknown high-resolution image by estimating the wavelet coefficients of the high frequency subbands. For that, a new approach is proposed in this paper, the contribution of which are twofold. First, unlike that the conventional Gaussian mixture (GM) model only exploits the magnitude information of the wavelet coefficients, a three-component exponential mixture (TCEM) model is proposed in this paper to investigate both the magnitude information and the sign information of the wavelet coefficients. The proposed TCEM model consists of a Gaussian component, a positive exponential component and a negative exponential component. Second, to address the parameter estimation challenge of the proposed TCEM model, the ant colony optimization (ACO) technique is exploited in this paper to classify the wavelet coefficients into one of three components of the proposed TCEM model for estimating their parameters. Experiments are conducted to demonstrate that the proposed approach outperform a number of approaches developed in the literature.

Image Sequence Interpolation Using Optimal Control

Abstract: The problem of finding an interpolating image between two given images in an image sequence is considered. The problem is formulated as an optimal control problem governed by a transport equation, i.e. we aim at finding a flow field which transports the first image as close as possible to the second image. This approach bears similarities with the Horn and Schunck method for optical flow calculation but in fact the model is quite different. The images are modeled in the space of functions of bounded variation and an analysis of solutions of transport equations in this space is included. Moreover, the existence of optimal controls is proven and necessary conditions are derived. Finally, two algorithms are given and numerical results are compared with existing methods. The new method is competitive with state-of-the-art methods and even outperforms several existing methods.

An Edge-Sensing Generic Demosaicing Algorithm With Application to Image Resampling

Abstract: In this paper, we introduce a new demosaicing algorithm that can be used for various sensor images captured by digital cameras equipped with various red-green-blue color filter arrays. Our algorithm enhances the universal demosaicing algorithm of Lukac et al by defining a new spectral interpolation model that exploits not only the information on the color of pixels but also the relative distance between neighboring pixels within an image. Moreover, we include an edge-detection model that makes our algorithm adaptive and reduces the presence of color shifts and artifacts. A series of tests has been made on images of the Kodak database, and our algorithm performs better than the universal demosaicing algorithm with regard to both subjective and objective evaluation. The versatility of our demosaicing algorithm is also highlighted through an application to the issue of color image resampling, and we obtain conclusive experimental results.

Partial Differential Equations for Zooming, Deinterlacing and Dejittering

Abstract: In this paper, for imaging applications, we introduce partial differential equations (PDEs), which allow for correcting displacement errors, for dejittering, and for deinterlacing, respectively, in multi-channel data. These equations are derived via semi-groups for non-convex energy functionals. As a particular example, for gray valued data, we find the mean curvature equation and the corresponding non-convex energy functional. As a further application for correction of displacement errors we study image interpolation, in particular zooming, of digital color images. For actual image zooming, the solutions of the proposed PDEs are projected onto a space of functions satisfying interpolation constraints. A comparison of the test results with standard and state-of-the-art interpolation algorithms shows the competitiveness of this approach.

Similarity modulated block estimation for image interpolation

Abstract: Modeling the nonstationarity of image signals is one of the challenging issues for image interpolation In this paper, we propose a similarity probability modeling to faithfully characterize the nonstationarity of image signals, and present a novel image interpolation algorithm based on the proposed model The missing pixels are estimated in groups by weighted block estimation The weight of each pixel inside the block is defined as the similarity probability between itself and the centered to-be-interpolated pixel It is demonstrated by the experimental results that the proposed method preserves the edge structures of the interpolated images better than the state-of-the-art interpolation methods Annoying artifacts nearby the sharp edges are also greatly reduced

Image Interpolation with Geometric Contour Stencils

Abstract: We consider the image interpolation problem where, given an image v with uniformly-sampled pixels vm,n and point spread function h, the goal is to find a function u(x,y) satisfying vm,n = (h ∗u)(m,n) for all m,n ∈ Z (1) so that u approximates the underlying function from which v was sampled. This article improves upon the IPOL article “Image Interpolation with Contour Stencils” [7]. In the previous work, contour stencils are used to estimate the image contours locally as short line segments. This article begins with a continuous formulation of total variation integrated over a collection of curves and defines contour stencils as a consistent discretization. This discretization is more reliable than the previous approach and can effectively distinguish contours that are locally shaped like lines, curves, corners, and circles. These improved contour stencils sense more of the geometry in the image. Interpolation is performed using an extension of the method described in the previous article. Using the improved contour stencils, there is an increase in image quality while maintaining similar computational efficiency.

System-on-Chip Solution of Video Stabilization for CMOS Image Sensors in Hand-Held Devices

Abstract: Majority of CMOS image sensors in consumer market utilize a rolling shutter to increase sensitivity. However, it causes severe distortions, such as jitter, wobble, or skew. Since most of these kinds of sensors are used in hand-held devices, the approach of undistorting and generating stabilized images is restricted to resource limited systems. It has also been one of the major challenges to have a mathematical representation of CMOS rolling effect depicting the practical scenario, while keeping accuracy and stability. We propose that a CMOS sensor can be modeled by a section-wise charge-coupled devices model which has multiple homographies and exploit the observation that rolling shutter mechanism gives close relationships among them. We present a CMOS seven-parameter model, and show video stabilization algorithm by the iterative parameter estimation technique. We address four issues while accelerating our stabilization algorithm within resource limited environment: accuracy, stability, computation time, and resource utilization. We developed cache based optimization techniques to meet the requirement of the memory bandwidth and computational time for the iterative parameter estimation and final output image interpolation, and also proposed the incremental form of the seven-parameter model to greatly reduce resource consumption while maintaining the same results as the previous. The validity and effectiveness of our approach is demonstrated by experiments for different types of camera motions. The cache based optimization technique can be used to accelerate other types of iterative vision algorithms that require repetitive memory access: feature tracking, motion estimation, motion compensation, various types of image distortion correction, and also image warping and scaling.

Apparatus for color interpolation using adjustable threshold

Abstract: An apparatus for color interpolation using an adjustable threshold is disclosed The color interpolation apparatus calculates the difference between the maximum value and the minimum value of the elements of the image data and determines the color interpolation method of the image data depending on the difference to perform the corresponding color interpolation With the present invention, the improved image quality can be provided because the color interpolation can be performed as a user desires

Learning context-aware sparse representation for single image super-resolution

Abstract: This paper presents a novel learning-based method for single image super-resolution (SR). Given an input low-resolution image and its image pyramid, we propose to perform context-constrained image segmentation and construct an image segment dataset with different context categories. By learning context-specific image sparse representation, our method aims to model the relationship between the interpolated image patches and their ground truth pixel values from different context categories via support vector regression (SVR). To synthesize the final SR output, we upsample the input image by bicubic interpolation, followed by the refinement of each image patch using the SVR model learned from the associated context category. Unlike prior learning-based SR methods, our approach does not require the reoccurrence of similar image patches (within or across image scales), and we do not need to collect training low and high-resolution image data in advance either. Empirical results show that our proposed method is quantitatively and qualitatively more effective than existing interpolation or learning-based SR approaches.

Flowlab - An Interactive Tool for Editing Dense Image Correspondences

Abstract: Finding dense correspondences between two images is a well-researched but still unsolved problem. For various tasks in computer graphics, e.g. image interpolation, obtaining plausible correspondences is a vital component. We present an interactive tool that allows the user to modify and correct dense correspondence maps between two given images. Incorporating state-of-the art algorithms in image segmentation, correspondence estimation and optical flow, our tool assists the user in selecting and correcting mismatched correspondences.

An evolutionary game-theoretic approach for image interpolation

Abstract: In this paper, we study the image interpolation from the game theoretic perspective and formulate the image interpolation problem as an evolutionary game. In this evolutionary game, the players are the unknown high resolution pixels and the pure strategies of the players are the corresponding low resolution neighbors. By regarding the non-negative weights of the low resolution pixels as the probabilities of selecting the pure strategies, the problem of estimating the high resolution pixels becomes finding the evolutionarily stable strategies for the evolutionary game. Experimental results show that the proposed game theoretical approach can achieve better performance than the state-of-the-art image interpolation methods in terms of both PSNR and visual quality.

Image interpolation methods evaluation for division of focal plane polarimeters

Abstract: This paper presents different imaging interpolation methods implemented for the division of focal plane polarization imaging sensor. The targeted polarization imaging sensor is a CCD based sensor with 1-Mega pixels resolution operating from 400nm to 1050nm wavelength. The five interpolation methods considered in this paper are: bilinear, weighted bilinear, bicubic spline, an approximated bicubic spline and a bicubic interpolation method. Test images of the five different interpolation methods as well as numerical error analysis are presented. Based on the comparison results, the full frame bicubic spline interpolation achieves the best performance for polarization images.

Image processing device, endoscope system, program, and image processing method

Abstract: An image processing device includes a demosaicing section that receives an image that includes a first color signal, a second color signal, and a third color signal, and performs an interpolation process that interpolates a missing color signal among the first color signal, the second color signal, and the third color signal on each pixel of the image, and an image output section that outputs an output image based on an image obtained by the interpolation process and output from the demosaicing section. The demosaicing section performs a space-variant interpolation process that implements a different interpolation process depending on a pixel position on a first image that has information within a first wavelength band. The demosaicing section performs a space-invariant interpolation process that implements an interpolation process independently of a pixel position on a second image that has information within a second wavelength band.

Forward interpolation approach for constructing a second version of an image from a first version of the image

Abstract: A forward interpolation approach is disclosed for enabling a second version of an image to be constructed from a first version of the image. According to one implementation of the forward interpolation approach, pixels from the first version of the image are processed one row at a time. As the pixels in a row of pixels in the first version of the image are processed, they may cause pixel values on different rows of the second version of the image to be determined. Since the pixel values of the second version of the image are stored in output line buffers, this means that, at any particular point in time, there may be multiple partially filled output line buffers. It has been observed that the forward interpolation approach enables significant benefits (such as reduced storage requirements and reduced internal bandwidth and processing) to be achieved over a backward interpolation approach.

Digital three-dimensional oscilloscope with real-time waveform image scaling function

Abstract: The invention discloses a digital three-dimensional oscilloscope with a real-time scaling function. All original waveform data before extraction corresponding to a test count sample value which is fed into a waveform image processor and is subjected to three-dimensional waveform image drawing is parallelly fed into a high-capacity storage to be stored synchronously in a mode of combining a three-dimensional image processing technology and a high-capacity data storage technology and in a screen brushing cycle of the digital three-dimensional oscilloscope. Thus, after the waveform mapping is finished, the waveform processor still can flexibly read the original waveform data from the high-capacity storage again according to the user operation and display requirements, and draws a new three-dimensional waveform image quickly according to different scaling extraction sample values, so that the digital three-dimensional oscilloscope performs image scaling and other operation on any capturedsignal while guaranteeing high waveform capturing rate, and the three-dimensional waveform image display mode is kept all the time and the captured signal details are not lost.

Discrete Curvelet Transform Based Super-resolution using Sub-pixel Image Registration

Abstract: All the time, there is a demand of High-Resolution (HR) images in electronic imaging applications. Super-Resolution (SR) is an approach used to restore High-Resolution (HR) image from one or more Low-Resolution (LR) images. The goal of SR is to extract the independent information from each LR image in that set and combine the information into a single high resolution (HR) image. The quality of reconstructed SR image obtained from a set of LR images depends upon the registration accuracy of LR images. In this paper SR reconstruction using a sub-pixel shift image registration and Fast Discrete Curvelet transform (FDCT) for image interpolation is proposed. The Curvelet transform is a multiscale pyramid with many directions and positions at each scale. Image interpolation is performed at the finest level in Curvelet domain. Experimentation based results have shown appropriate improvements in PSNR and MSE. Also, it is experimentally verified that the computational complexity of the SR algorithm is reduced.

Image interpolation method and apparatus

Abstract: The present invention relates to an image interpolation method and apparatus. The image interpolation method according to the present invention involves selecting different interpolation filters depending on the location of a sub-pixel between integer pixels, and generates a sub-pixel value at the location of the sub-pixel using the selected interpolation filter.

Efficient implementation of a cubic-convolution based image scaling engin

Abstract: In video applications, real-time image scaling techniques are often required. In this paper, an efficient implementation of a scaling engine based on 4×4 cubic convolution is proposed. The cubic convolution has a better performance than other traditional interpolation kernels and can also be realized on hardware. The engine is designed to perform arbitrary scaling ratios with an image resolution smaller than 2560×1920 pixels and can scale up or down, in horizontal or vertical direction. It is composed of four functional units and five line buffers, which makes it more competitive than conventional architectures. A strict fixed-point strategy is applied to minimize the quantization errors of hardware realization. Experimental results show that the engine provides a better image quality and a comparatively lower hardware cost than reference implementations.

Image resolution enhancement based on edge directed interpolation using dual tree — Complex wavelet transform

Abstract: Image resolution enhancement is a usable process for many image processing applications such as geoscience studies, astronomy and geographical information systems. One of the traditional methods used to increase the image resolution is image interpolation but the potential problem associated with it is to magnify the image many times without loss in image clarity. However, all the classical linear interpolation techniques like bilinear, bi-cubic interpolation methods generate blurred image. By employing dual-tree complex wavelet transform (DT-CWT) on a edge directional interpolation, it is possible to recover the high frequency components which provides an image with good visual clarity and thus super resolved high resolution images are obtained. The obtained simulation results comply with the above stated claim. A performance comparison of it is made with the recent work discussed in [7].

Robust edge-directed interpolation of magnetic resonance images.

Abstract: Image interpolation is intrinsically a severely under-determined inverse problem. Traditional non-adaptive interpolation methods do not account for local image statistics around the edges of image structures. In practice, this results in artifacts such as jagged edges, blurring and/or edge halos. To overcome this shortcoming, edge-directed interpolation has been introduced in different forms. One variant, new edge-directed interpolation (NEDI), has successfully exploited the 'geometric duality' that links the low-resolution image to its corresponding high-resolution image. It has been demonstrated that for scalar images, NEDI is able to produce better results than non-adaptive traditional methods, both visually and quantitatively. In this work, we return to the root of NEDI as a least-squares estimation method of neighborhood patterns and propose a robust scheme to improve it. The improvement is twofold: firstly, a robust least-squares technique is used to improve NEDI's performance to outliers and noise; secondly, the NEDI algorithm is extended with the recently proposed non-local mean estimation scheme. Moreover, the edge-directed concept is applied to the interpolation of multi-valued diffusion-weighted images. The framework is tested on phantom scalar images and real diffusion images, and is shown to achieve better results than the non-adaptive methods as well as NEDI, in terms of visual quality as well as quantitative measures.

Projection onto convex sets super-resolution image reconstruction based on wavelet bi-cubic interpolation

Abstract: The traditional projections onto convex sets super-resolution image reconstruction algorithm leads to the halo effect in reconstructed high resolution image, so we present a super-resolution reconstruction method based on wavelet bicubic interpolation algorithm. The new algorithm utilizes wavelet bi-cubic interpolation algorithm to obtain the initial image of POCS algorithm and introduces bilateral filter to estimate point spread function. Bilateral filtering makes non-edge pixel having minimum influence on the edge region. Algorithm also uses adaptive relaxation parameters to reduce the impact of error motion information. Compared with traditional algorithm, experiment results show that the proposed reconstruction algorithm eliminates the halo effect obviously, thus the reconstructed image can achieve a good visual effect.