An Edge-based Image Interpolation Approach Using Symmetric Biorthogonal Wavelet Transform

Image interpolation is used widely in the computer vision. Holding edge information is main problem in the image interpolation. By using bilinear and bicubic B-spline interpolation methods, a novel image interpolation approach was proposed in this paper. Firstly, inverse distance weighted average method was used to reduce image’s noise. Secondly, edge detection operator was used to extract image's edges information. It can help us to select different interpolation methods in the image interpolation process. Finally, we selected bilinear interpolation approach at non-edge regions, and bicubic B-spline interpolation method was used near edges regions. Further more, control vertexes were computed from pixels with calculation formula which has been simplified in the B-spline interpolation process. Experiments showed the interpolated image by the proposed method had good vision results for it could hold image's edge information effectively.

Latent Embedded Graphs for Image and Shape Interpolation

In this contribution we present an optimal control approach for physics-based optical flow estimation and image interpolation. The aim of the developed process is to identify appropriate boundary data of an underlying physical model describing the transport field, which reason the movement of an initial brightness distribution. Thereby, the flow field as solution of the time-dependent non-linear Navier-Stokes equations is coupled to a transport dominant convection-diffusion equation describing the brightness intensity. Thus, we have to deal with a weakly coupled PDE system as state equation of a PDE constrained optimisation problem. The data is given in form of consecutive images, with a sparse temporal resolution, representing the brightness distribution at different time points. We will present the mathematical theory of the resulting optimisation problem, which is based on a Robin-type boundary control. We describe the numerical solution process and present by means of synthetical test cases the functionality of the method. Finally we discuss the application of multiple shooting techniques for the considered problem, since we observed that the employed Newton-type method is very sensitive with respect to the chosen initial value.

We present a perception-based method for image interpolation, aiming for perceptually convincing transitions between real-world images. Without 3D geometry or scene motion, perception-based image interpolation enables smooth viewpoint navigation across space and time. We show how global visual effects can be created from a collection of unsynchronized, uncalibrated images. A user study confirms the perceptual quality of the proposed image interpolation approach.

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.

When interpolating images in the wavelet domain, the main problem is how to estimate the finest detail coefficients. Wavelet coefficients across scales have an interscale dependency, and the dependency varies according to the local energy of the coefficients. This implies the possible existence of functional mappings from one scale to another scale. If we can estimate the mapping parameters from the observed coefficients, then it is possible to predict the finest detail coefficients. In this article, we use the multilayer perceptron (MLP) neural networks to learn a mapping from the coarser scale to the finer scale. When exploiting the MLP neural networks, phase uncertainty, a well-known drawback of wavelet transforms, makes it difficult for the networks to learn the interscale mapping. We solve this location ambiguity by using a phase-shifting filter. After the single-level phase compensation, a wavelet coefficient vector is assigned to one of the energy-dependent classes. Each class has its corresponding network. In the simulation results, we show that the proposed scheme outperforms the previous wavelet-domain interpolation method as well as the conventional spatial domain methods.

We present a unified framework for coupling the EM algorithm with the Bayesian hierarchical modeling of neighboring wavelet coefficients of image signals. Within this framework, problems with missing pixels or pixel components, and hence unobservable wavelet co-efficients, are handled simultaneously with denoising. The hyper-parameters of the model are estimated via the marginal likelihood by the EM algorithm, and a part of the output of its E-step automatically provide optimal estimates, given the specified Bayesian model, of the noise-free image. This unified empirical-Bayes based framework, therefore, offers a statistically principled and extremely flexible approach to a wide range of pixel estimation problems including image denoising, image interpolation, demosaicing, or any combinations of them.

In multimedia communication and data storage, compression of data is essential to speed up the transmission rate, minimize the use of channel bandwidth, and minimize storage space. The drawback of compression is that compressed data are more vulnerable to channel noise during transmission. In this work, error concealment methodology is proposed that has the ability of both error detection and concealment, and thus guarantees minimum use of channel bandwidth. The methodology requires transmission of edge information of the original image along with JPEG2000 compressed image. At the receiver, the edge information of received wavelet coefficients is computed and compared with the received edge information of the original image to determine the corrupted coefficients. Three methods of concealment, each including a filter, are investigated to handle the corrupted regions/coefficients. The simulation results conducted on test images for different values of bit error rate are presented to demonstrate the performance of the proposed approach. The objective quality measure such as peak-signal-to-noise ratio (PSNR), root-mean-square error (rms) and subjective quality measure are used to evaluate processed images.

Distributed video sensors usually use fairly low-resolution images due to the limited transmission bandwidth in transmitting images. It is potential to enhance the captured low-resolution images using image resolution enhancement technique that is able to produce a high-resolution image from its low-resolution counterpart. The key challenge of image resolution enhancement is to preserve the edge structure in images. In this paper, a new image resolution enhancement approach is proposed to estimate the pixel using a bilateral weighted average of its neighboring pixels. First, the neighboring pixels with nearer distance have larger contributions. Second, the neighboring pixels belonging to direction with smaller variation have larger contributions. Experimental results are provided to show that the proposed approach outperforms several conventional edge-directed image interpolation algorithms. Furthermore, the proposed approach yields low computational complexity; it is potential for real-time implementation.

We propose a novel image interpolation scheme based on prescribed tangent of cubic B-spline curve. Firstly a cubic B-spline interpolation curve is constructed by discrete pixels. Then a straight line is constructed by joining two points on the B-spline curve with respect to two neighboring pixels. The value of the interpolation point between the two points is determined based on the tangent of the B-spline curve, whose slope is equal to that of the above straight line. Finally, we use the Sobel operator to estimate the edge direction of the interpolation point. Experiments results indicate that the proposed method can greatly maintain the basic information of the original image, reduce the effects of image smoothing and edge blurring caused by the traditional cubic B-spline interpolation, and improve the sharpness of the edges.

In order to enhance the resolution of the image and improve the visual effect of the image, a method of image quality enhancement based on bilinear interpolation and wavelet transformation is proposed. The algorithm first enhances the image by histogram equalization and bilinear interpolation to preserve better detail quality, and then perform wavelet transform on the histogram equalized image and the original imageeach of the input images are devided into 4 subbands, estimates the singular value matrix of the low frequency subband image, and then enhances the image through Wavelet reconstruction, Finally, perform histogram matching on the result image and bilinear interpolation image to obtain an enhanced image. The experimental results show that the performance of the proposed method is better than that of the traditional method.

The performance of interpolation algorithm used in bi-dimensional empirical mode decomposition directly affects its popularization and application. Therefore, the research on interpolation algorithm is more reasonable, accurate and fast. So far, in the interpolation algorithm adopted by the bi-dimensional empirical mode decomposition, an adaptive interpolation algorithm can be proposed according to the image characteristics. In view of this, this paper proposes an image interpolation algorithm based on the particle swarm and fractal. Its procedure includes: to analyze the given image by using the fractal brown function, to pick up the feature quantity from the image, and then to operate the adaptive image interpolation in terms of the obtained feature quantity. The parameters involved in the interpolation process are optimized by particle swarm optimization algorithm, and the optimal parameters are obtained, which can solve the problem of low efficiency and low precision of interpolation algorithm used in bi-dimensional empirical mode decomposition. It solves the problem that the image cannot be decomposed to obtain accurate and reliable bi-dimensional intrinsic modal function, and realize the fast decomposition of the image. It lays the foundation for the further popularization and application of the bi-dimensional empirical mode decomposition algorithm.

In this paper, a new reversible data hiding scheme has been proposed using lagrange’s interpolating polynomial on interpolated sub-sampled images. First, we generate sub-sampled images from original image and enlarge its size using image interpolation. Now, we convert secret message using lagrange interpolating polynomial and generate new secret message. The new secret message is divided and stored within interleaved pixel of each interpolated sub-sampled images. At the receiver end, new secret message is extracted from interleaved pixel of each sub-sampled stego images and then lagrange’s interpolation is applied to generate original secret message. The security has been enhanced due to the distributive nature of hidden data within multiple images. The original pixels are not effected during data embedding which assure reversibility. The proposed scheme provides average embedding capacity with good visual quality measured by peak signal to noise ratio (PSNR) which is greater than 50 dB. It is observed that the proposed scheme provides better performance than other existing data hiding schemes in terms of data embedding capacity, visual quality and security. We have analyzed our stego images through RS analysis, calculate relative entropy, standard deviation and correlation coefficient of original and stego image to show the robustness under various steganographic attacks.

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.

Although ordinary least squares (OLS) regression achieves great success in clean image interpolation, its effectiveness is questionable in the scenario of web images which are usually compressed beforehand. The inherent flaw of OLS is that it is asymmetric, the perturbation is only confined on the right side of the linear system. It is not reasonable for web images. Considering the drawback of OLS, in this paper, we propose an efficient web image interpolation algorithm based on total least squares (TLS) regression. In the proposed method, small perturbations are allowed in both side of the system, which are optimized by TLS in a patch-based manner. Furthermore, we develop a weighted version of TLS to consider contribution diversity of different samples and patches in model estimation, which can efficiently remove the influence of outliers in regression. Experimental results on benchmark test images demonstrate the efficiency of our method.