Low complexity image interpolation method based on path selection

Enhancement of radar rainfall estimates for urban hydrology through optical flow temporal interpolation and Bayesian gauge-based adjustment

Optical flow is widely used for describing motion cues in the scene, but limited by slow estimating speed and illumination sensitivity. To handle both problems, this paper focuses on improving speed and accuracy of optical flow using RGB-D data and enhancing its robustness on motion description via fusing depth flow which is obtained only using depth data. First, salient motion regions (SMRs) are detected between depth frames which have good character on motion description for they all locate on moving objects. Then, depth flow is calculated to describe 3D motion for each SMR and directs fast orientation region growing on depth map. Thus larger motion regions are grown, and region-based optical flow estimation is conducted on grown regions. Estimation error is reduced and noise is inhibited due to depth constraints. Finally, a fusion scheme is adopted which combines depth flow and optical flow for better 3D motion description in the scene. Experiments on a RGB-D video data sets recorded in various complex scenes demonstrate the improved speed and robustness of the proposed method.

An efficient general data hiding scheme based on image interpolation

Accurate inverse-consistent symmetric optical flow for 4D CT lung registration

Gradient-based optical flow estimation methods such as LucasKanade method work well for scenes with small displacements but fail when objects move with large displacements. Hierarchical matching-based methods do not suffer from large displacements but are less accurate. By utilizing the high speed imaging capability of CMOS image sensors, the frame rate can be increased to obtain more accurate optical flow with wide range of scene velocities in real time. Further, by integrating the memory and processing with the sensor on the same chip, optical flow estimation using high frame rate sequences can be performed without unduly increasing the off-chip data rate. The paper describes a method for obtaining high accuracy optical flow at a standard frame rate using high frame rate sequences. The Lucas-Kanade method is used to obtain optical flow estimates at high frame rate, which are then accumulated and refined to obtain optical flow estimates at a standard frame rate. The method is tested on video sequences synthetically generated by perspective warping. Results demonstrate significant improvements in optical flow estimation accuracy with moderate memory and computational power requirements.

Optical flow has been used for matching or tracking of individual image objects through a time sequence of images and is applied to the problem of image interpolation by treating the serial slice images as a spatial sequence. Calculation of optical flow between two images results in a 'velocity vector map' indicating the relative displacement between similar structures in both images. Thus, individual vectors are used to match points in adjoining images and interpolate between their corresponding intensities. The interpolated intensity is then redistributed and normalized by unit area in the interpolated slice. The accuracy of optical flow interpolation is evaluated by generating an interpolated slice, i', from original slices i-l and i+l and then comparing i' with the original middle scanned slice, i. Optical flow interpolation compares favorably to linear interpolation both visually and quantitatively. Quantitative comparison, i-i', shows a 253% improvement over linear interpolation for synthetic binary images and a 13% improvement over linear interpolation for grayscale CT images.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In this paper a variational method for registering or mapping like points in medical images is proposed and analyzed. The proposed variational principle penalizes a departure from rigidity and thereby provides a natural generalization of strictly rigid registration techniques used widely in medical contexts. Difficulties with finite displacements are elucidated, and alternative infinitesimal displacements are developed for an optical flow formulation which also permits image interpolation. The variational penalty against non-rigid flows provides sufficient regularization for a well-posed minimization and yet does not rule out irregular registrations corresponding to an object excision. Image similarity is measured by penalizing the variation of intensity along optical flow trajectories. The approach proposed here is also independent of the order in which images are taken. For computations, a lumped finite element Eulerian discretization is used to solve for the optical flow. Also, a Lagrangian integration of the intensity along optical flow trajectories has the advantage of prohibiting diffusion among trajectories which would otherwise blur interpolated images. The subtle aspects of the methods developed are illustrated in terms of simple examples, and the approach is finally applied to the registration of magnetic resonance images.

To assess added value of a new time-resolved technique with temporal interpolation and stochastic spiral trajectory through k-space and parallel imaging (TR-MRA) to conventional bolus chase MRA (BC-MRA) for infragenual peripheral artery evaluation. An institutional review board-approved retrospective review of peripheral arterial disease patients was performed. Infragenual TR-MRA and BC-MRA were performed in 26 patients over four months. Two readers individually assessed image quality, diagnostic confidence, and stenosis severity and length in 13 defined below knee segments, first with BC-MRA alone, and then with a combined BC-MRA and TR-MRA reading (BC+TR-MRA). Perceived contribution of TR-MRA was rated by each reader. The reference standard was a consensus reading of both sequences. Catheter angiographic (CA) correlation was available in 6 patients. A total of 646 infragenual segments in 51 extremities were evaluated. Image quality and diagnostic confidence were superior for BC+TR-MRA compared with BC-MRA alone (P < 0.001). Adding TR-MRA improved sensitivity (85.7% versus 80.7%; P < 0.05) and diagnostic accuracy (88.1% versus 85.4%; P < 0.05) for hemodynamically significant stenosis. Venous contamination (0% versus 13.1% segments) and motion (0.9% versus 8.0%) were decreased for BC+TR-MRA versus BC-MRA alone, P < 0.01. For BC+TR-MRA, TR-MRA was rated more useful than BC-MRA in 30/51 legs (58.8%). TR-MRA identified retrograde flow in 5 segments. Where available, there was high concordance between CA and BC+TR-MRA (91.6%) for stenosis. Adding TR-MRA with temporal interpolation and stochastic spiral trajectories to bolus chase MRA improves image quality, diagnostic confidence and accuracy. It provides hemodynamic information and minimizes venous contamination and patient motion.

A motion estimation algorithm using wavelet approximation as an optical flow model has been developed to estimate accurate dense optical flow from an image sequence. This wavelet motion model is particularly useful in estimating optical flows with large displacement. Traditional pyramid methods which use the coarse-to-fine image pyramid by image burring in estimating optical flow often produce incorrect results when the coarse-level estimates contain large errors that cannot be corrected at the subsequent finer levels. This happens when regions of low texture become flat or certain patterns result in spatial aliasing due to image blurring. Our method, in contrast, uses large-to-small full-resolution regions without blurring images, and simultaneously optimizes the coarser and finer parts of optical flow so that the large and small motion can be estimated correctly. We compare results obtained by using our method with those obtained by using one of the leading optical flow methods, the Szeliski pyramid spline-based method. The experiments include cases of small displacement (less than 4 pixels under 128/spl times/128 image size or equivalent displacement under other image sizes), and those of large displacement (10 pixels). While both methods produce comparable results when the displacements are small, our method outperforms pyramid spline-based method when the displacements are large.

Data hiding is a technique that embeds a cluster of secret messages into the original image. The image with secret messages can be distributed on the Internet while the message embedded would not be easily discovered by a third party. In this way, the secret message can be well protected. At the same time, an image might be made with complex and smooth textures. If changes are made on complex textures, it is less easy for human eyes to discern the differences in the image; but if changes are made on smooth textures, the changes are easier to be discerned by human eyes. This paper proposes a data hiding method based on reduplicated exploiting modification direction (REMD), image interpolation, and canny edge detection. It aims at fulfilling two goals. First, conduct difference-embedding on the image's feature information, distribute the image, and use image interpolation to accomplish reversibility. Second, check the pixels that are located at the edge and insert different data payload according to the application demands. This allows users to flexibly adjust data payload embedded into the image's edge pixels according to their practical conditions, effectively considering both the image quality and payload. The experimental results demonstrate that the proposed method can achieve the data payload of 3.01bpp, so this is a reversible hiding technique with a very high embedding capacity. In the mean time, the average image quality is kept at an acceptable level, about 33?±?1 dB.

This paper presents a novel method for virtual view synthesis that allows viewers to virtually fly through real soccer scenes, which are captured by multiple cameras in a stadium. The proposed method generates images of arbitrary viewpoints by view interpolation of real camera images near the chosen viewpoints. In this method, cameras do not need to be strongly calibrated since projective geometry between cameras is employed for the interpolation. For avoiding the complex and unreliable process of 3-D recovery, object scenes are segmented into several regions according to the geometric property of the scene. Dense correspondence between real views, which is necessary for intermediate view generation, is automatically obtained by applying projective geometry to each region. By superimposing intermediate images for all regions, virtual views for the entire soccer scene are generated. The efforts for camera calibration are reduced and correspondence matching requires no manual operation; hence, the proposed method can be easily applied to dynamic events in a large space. An application for fly-through observations of soccer match replays is introduced along with the algorithm of view synthesis and experimental results. This is a new approach for providing arbitrary views of an entire dynamic event.

Super-resolution imaging (S.R.) is a series of techniques that enhance the resolution of an imaging system, especially in surveillance cameras where simplicity and low cost are of great importance. S.R. image reconstruction can be viewed as a three-stage process: image interpolation, image registration, and fusion. Image interpolation is one of the most critical steps in the S.R. algorithms and has a significant influence on the quality of the output image. In this paper, two hardware-efficient interpolation methods are proposed for these platforms, mainly for the mobile application. Experiments and results on the synthetic and real image sequences clearly validate the performance of the proposed scheme. They indicate that the proposed approach is practically applicable to real-world applications. The algorithms are implemented in a Field Programmable Gate Array (FPGA) device using a pipelined architecture. The implementation results show the advantages of the proposed methods regarding area, performance, and output quality.

Data hiding in multimedia objects such as text, images, audio and video clips is a technique that has been widely used to achieve security for applications requiring copyright protection, covert communication, and tampering detection. Data hiding can be irreversible or reversible, where the latter is used to ensure exact recovery of the media object after extracting the hidden data. Different approaches to achieve reversible data hiding have been proposed. One of the approaches is the interpolation-based data hiding which has been proposed to achieve high data hiding capacity. This paper presents a new computationally simple interpolation-based data hiding technique that increases data hiding capacity and limits the cover image distortion that is caused by the two major steps of interpolation-based techniques; the downscaling/expansion step and the data hiding step . Image distortion reduction in the downscaling/expansion step is achieved by using a new image interpolation algorithm, whereas the image distortion in the data hiding step is reduced utilizing a new adjustable data hiding algorithm, which adaptively adjusts the level of tradeoff between data hiding capacity and image quality. The performance of the proposed technique has been evaluated for data hiding capacity and image quality using three metrics: peak signal to noise ratio (PSNR), weighted PSNR (WPSNR), and structural similarity index (SSIM). The data hiding capacity achieved by the proposed technique can be as high as 1.7bpp, which is higher by 8.5 to 72% as compared to similar techniques. Moreover, for the data hiding step, the proposed algorithm achieved high quality images as reflected by values up to 54 dB for PSNR, 78 dB for WPSNR, and 0.9998 for SSIM.

Estimating the correspondence between the images using optical flow is the key component for image fusion, however, computing optical flow between a pair of facial images including backgrounds is challenging due to large differences in illumination, texture, color and background in the images. To improve optical flow results for image fusion, we propose a novel flow estimation method, wavelet flow, which can handle both the face and background in the input images. The key idea is that instead of computing flow directly between the input image pair, we estimate the image flow by incorporating multi‐scale image transfer and optical flow guided wavelet fusion. Multi‐scale image transfer helps to preserve the background and lighting detail of input, while optical flow guided wavelet fusion produces a series of intermediate images for further fusion quality optimizing. Our approach can significantly improve the performance of the optical flow algorithm and provide more natural fusion results for both faces and backgrounds in the images. We evaluate our method on a variety of datasets to show its high outperformance.

Reversible data hiding method enables to recover the cover image after the secret data were extracted. Recently, a reversible data hiding method based on image interpolation has been proposed. It can contain high capacity and keep good visual quality. In this study, an improved method has proposed to refine the interpolation image based method. Experimental results revealed that the proposed method has the same capacity and promoted the quality of stego-image when compared with the origin method.