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Fractional interpolation is used to provide sub-pixel level references for motion compensation in the interprediction of video coding, which attempts to remove temporal redundancy in video sequences. Traditional handcrafted fractional interpolation filters face the challenge of modeling discontinuous regions in videos, while existing deep learning-based methods are either designed for a single quantization parameter (QP), only generating half-pixel samples, or need to train a model for each sub-pixel position. In this paper, we present a one-for-all fractional interpolation method based on a grouped variation convolutional neural network (GVCNN). Our method can deal with video frames coded using different QPs and is capable of generating all sub-pixel positions at one sub-pixel level. Also, by predicting variations between integer-position pixels and sub-pixels, our network offers more expressive power. Moreover, we perform specific measurements in training data generation to simulate practical situations in video coding, including blurring the down-sampled sub-pixel samples to avoid aliasing effects and coding integer pixels to simulate reconstruction errors. In addition, we analyze the impact of the size of blur kernels theoretically. Experimental results verify the efficiency of GVCNN. Compared with HEVC, our method achieves 2.2% in bit saving on average and up to 5.2% under low-delay P configuration. [ABSTRACT FROM AUTHOR]

Balanced multiwavelets with interpolatory property are discussed in this paper. This kind of multiwavelets can have a sampling property like Shannon's sampling theorem. It has been shown that the corresponding matrix-valued refinable mask has special structure, and an orthogonal multifilter bank \H(z),G(z)\ can be reduced to a scalar valued conjugate quadrature filter (CQF) a(z). But it does not mean that any scalar CQF can form a “good” multifilter bank which can generate a vector-valued refinable function with some degree of smoothness. In the context of balanced multiwavelets, we give the definition of transferring balance order, which a scalar CQF a(z) satisfies, to guarantee that the multiwavelet \Psi generated is balanced. On the basis of the parametrization of a scalar CQF with any length and conditions of transferring balance order, parametrization of multifilter banks which can generate interpolatory multiwavelet and interpolatory scaling function, is gotten. Moreover, some balanced interpolatory multiwavelets have been constructed. Interpolatory analysis-ready multiwavelets (armlets) are also discussed in this paper. It is known that conditions of armlets are easy to validate, compared with balanced multiwavelets. But it will be present that if the corresponding scaling function \Phi is interpolatory, the multiwavelet \Psi is balanced of order n if and only if it is an armlet of order n. Finally, the application of balanced multiwavelets with interpolatory property in image processing is also discussed. [ABSTRACT FROM PUBLISHER]

Single sensor digital cameras use color filter arrays to capture a subset of the color data at each pixel coordinate. Demosaicing or color filter array (CFA) interpolation is the process of estimating the missing color samples to reconstruct a full color image. In this paper, we propose a demosaicing method that uses multiscale color gradients to adaptively combine color difference estimates from different directions. The proposed solution does not require any thresholds since it does not make any hard decisions, and it is noniterative. Although most suitable for the Bayer CFA pattern, the method can be extended to other mosaic patterns. To demonstrate this, we describe its application to the Lukac CFA pattern. Experimental results show that it outperforms other available demosaicing methods by a clear margin in terms of CPSNR and S-CIELAB measures for both mosaic patterns. [ABSTRACT FROM AUTHOR]

This paper proposes a new motion-compensated frame interpolation (MCFI) method. The proposed method utilizes a symmetric motion estimation (SME) method, which is a new pixel-wise motion estimation method for intermediate frame interpolation. By using an adaptive search range for the motion estimation, the proposed method can obtain a more reliable motion vector for each pixel than previous MCFI methods that use a conventional block matching algorithm (BMA). In addition, we propose a combined method of the SME and BMA to reduce the computation time of the pixel-wise motion estimation method. The experimental results show that the proposed method outperforms other MCFI methods in terms of generating objectively and subjectively better interpolated frames. [ABSTRACT FROM AUTHOR]

In this paper, a motion-aligned auto-regressive (MAAR) model is proposed for frame rate up conversion, where each pixel is interpolated as the average of the results generated by one forward MAAR (Fw-MAAR) model and one backward MAAR (Bw-MAAR) model. In the Fw-MAAR model, each pixel in the to-be-interpolated frame is generated as a linear weighted summation of the pixels within a motion-aligned square neighborhood in the previous frame. To derive more accurate interpolation weights, the aligned actual pixels in the following frame are also estimated as a linear weighted summation of the newly interpolated pixels in the to-be-interpolated frame by the same weights. Consequently, the backward-aligned actual pixels in the following frame can be estimated as a weighted summation of the corresponding pixels within an enlarged square neighborhood in the previous frame. The Bw-MAAR is performed likewise except that it is operated in the reverse direction. A damping Newton algorithm is then proposed to compute the adaptive interpolation weights for the Fw-MAAR and Bw-MAAR models. Extensive experiments demonstrate that the proposed MAAR model is able to achieve superior performance than the traditional frame interpolation methods such as MCI, OBMC, and AOBMC, and it is even better than STAR model for the most test sequences with moderate or large motions. [ABSTRACT FROM AUTHOR]

Minutiae are very important features for fingerprint representation, and most practical fingerprint recognition systems only store the minutiae template in the database for further usage. The conventional methods to utilize minutiae information are treating It as a point set and finding the matched points from different minutiae sets. In this paper, we propose a novel algorithm to use minutiae for fingerprint recognition, in which the fingerprint's orientation field Is reconstructed from minutiae and further utilized in the matching stage to enhance the system's performance. First, we produce "virtual" minutiae by using interpolation in the sparse area, and then use an orientation model to reconstruct the orientation field from all "real" and "virtual" minutiae. A decision fusion scheme is used to combine the reconstructed orientation field matching with conventional minutiae-based matching. Since orientation field is an important global feature of fingerprints, the proposed method can obtain better results than conventional methods. Experimental results illustrate its effectiveness. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for cost aggregation and occlusion handling for stereo matching. In order to estimate optimal cost, given a per-pixel difference image as observed data, we define an energy function and solve the minimization problem by solving the iterative equation with the numerical method. We improve performance and increase the convergence rate by using several acceleration techniques such as the Gauss-Seidel method, the multiscale approach, and adaptive interpolation. The proposed method is computationally efficient since it does not use color segmentation or any global optimization techniques. For occlusion handling, which has not been performed effectively by any conventional cost aggregation approaches, we combine the occlusion problem with the proposed minimization scheme. Asymmetric information is used so that few additional computational loads are necessary. Experimental results show that performance is comparable to that of many state-of-the-art methods. The proposed method is in fact the most successful among all cost aggregation methods based on standard stereo test beds. [ABSTRACT FROM AUTHOR]

In this paper, a new weighted adaptive lifting (WAL)-based wavelet transform is presented. The proposed WAL approach is designed to solve the problems existing in the previous adaptive directional lifting (ADL) approach, such as mismatch between the predict and update steps, interpolation favoring only horizontal or vertical direction, and invariant interpolation filter coefficients for all images. The main contribution of the proposed approach consists of two parts: one is the improved weighted lifting, which maintains the consistency between the predict and update steps as far as possible and preserves the perfect reconstruction at the same time; another is the directional adaptive interpolation, which improves the orientation property of the interpolated image and adapts to statistical property of each image. Experimental results show that the proposed WAL-based wavelet transform for image coding outperforms the conventional lifting-based wavelet transform up to 3.06 dB in PSNR and significant improvement in subjective quality is also observed. Compared with the ADL-based wavelet transform, up to 1.22-dB improvement in PSNR is reported. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm based on the Mumford-Shah model for simultaneously detecting the edge features of two images and jointly estimating a consistent set of transformations to match them. Compared to the current asymmetric methods in the literature, this fully symmetric method allows one to determine one-to-one correspondences between the edge features of two images. The entire variational model is realized in a multiscale framework of the finite element approximation. The optimization process is guided by an estimation minimization-type algorithm and an adaptive generalized gradient flow to guarantee a fast and smooth relaxation. The algorithm is tested on Ti and T2 magnetic resonance image data to study the parameter setting. We also present promising results of four applications of the proposed algorithm: interobject monomodal registration, retinal image registration, matching digital photographs of neurosurgery with its volume data, and motion estimation for frame interpolation. [ABSTRACT FROM AUTHOR]

In this paper, a motion-compensated de-interlacing algorithm using the Markov random field (MRF) model is proposed. The de-interlacing problem is formulated as a maximum a posteriori (MAP) MRF problem. The MAP solution is the one that minimizes an energy function, which imposes discontinuity-adaptive smoothness (DAS) spatial constraint on the de-interlaced frame. The edge direction information, which is used to formulate the DAS constraint, is implicitly indicated by weight vectors (weights for 16 digitized directions). Generally, large weights are assigned to along-edge directions and relatively small weights are assigned to across-edge directions. As a local statistical-based method, the proposed weighting method should be more robust than traditional edge-directed interpolation methods in deciding local edge directions. The proposed algorithm is implemented by an iterative optimization process, which guarantees convergence. However, a global optimal solution is not guaranteed due to computational complexity concern. Simulation results compare the proposed algorithm to other motion compensated de-interlacing algorithms. Significant improvements of de-interlaced edges are observed. [ABSTRACT FROM AUTHOR]

Image moments have been extensively used as feature descriptors. However, the quantization error introduced in discrete signals presents problems, especially when dealing with small-size images. This results in the fact that the invariant properties of moments are compromised. In this paper, we present a technique suitable for the calculation of moments from a continuous signal, derived by piecewise polynomial interpolation of the corresponding discrete one. The computed moments exhibit significantly increased accuracy while requiring trivial computational effort. Zernike moments are then computed using the proposed scheme and are shown to display increased stability to geometrical transformations. [ABSTRACT FROM AUTHOR]

This paper proposes a new interpolation method, called the error-amended sharp edge (EASE) scheme, which is a modified bilinear method. In order to remove/reduce interpolation artifacts such as image blur and the checkerboard effect (ringing), EASE tries to amend the interpolation error by employing the classical interpolation error theorem in an edge-adaptive fashion. EASE is applied for image zooming by both integer and noninteger magnification factors. The new interpolation scheme has proved to result in high-resolution images having clearer and sharper edges than linear interpolation methods, for all synthetic and natural images we have tested. EASE can be implemented with ease; it turns out to be similarly efficient as cubic interpolation schemes. [ABSTRACT FROM AUTHOR]

Character skeleton plays a significant role in character recognition. The strokes of a character may consist of two regions, i.e., singular and regular regions. The intersections and junctions of the strokes belong to singular region, while the straight and smooth parts of the strokes are categorized to regular region. Therefore, a skeletonization method requires two different processes to treat the skeletons in theses two different regions. All traditional skeletonization algorithms are based on the symmetry analysis technique. The major problems of these methods are as follows. 1) The computation of the primary skeleton in the regular region is indirect, so that its implementation is sophisticated and costly. 2) The extracted skeleton cannot be exactly located on the central line of the stroke. 3) The captured skeleton in the singular region may be distorted by artifacts and branches. To overcome these problems, a novel scheme of extracting the skeleton of character based on wavelet transform is presented in this paper. This scheme consists of two main steps, namely: a) extraction of primary skeleton in the regular region and b) amendment processing of the primary skeletons and connection of them in the singular region. A direct technique is used in the first step, where a new wavelet-based symmetry analysis is developed for finding the central line of the stroke directly. A novel method called smooth interpolation is designed in the second step, where a smooth operation is applied to the primary skeleton, and, thereafter, the interpolation compensation technique is proposed to link the primary skeleton, so that the skeleton in the singular region can be produced. Experiments are conducted and positive results are achieved, which show that the proposed skeletonization scheme is applicable to not only binary image but also gray-level image, and the skeleton is robust against noise and affine transform. [ABSTRACT FROM AUTHOR]

In this paper, the design of the error resilient time-domain lapped transform is formulated as a linear minimal mean-squared error problem. The optimal Wiener solution and several simplifications with different tradeoffs between complexity and performance are developed. We also prove the persymmetric structure of these Wiener filters. The existing mean reconstruction method is proven to be a special case of the proposed framework. Our method also includes as a special case the linear interpolation method used in DCT-based systems when there is no pre/post-filtering and when the quantization noise is ignored. The design criteria in our previous results are scrutinized and improved solutions are obtained. Various design examples and multiple description image coding experiments are reported to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we make contact with the field of non-parametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some popular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples. [ABSTRACT FROM AUTHOR]

This paper introduces edge-forming schemes for image zooming of color images by general magnification factors. In order to remove/reduce artifacts arising in image interpolation, such as image blur and the checkerboard effect, an edge-forming method is suggested to be applied as a postprocess of standard interpolation methods. The method is based on nonconvex non-linear partial differential equations. The equations are carefully discretized, incorporating numerical schemes of anisotropic diffusion, to be able to form reliable edges satisfactorily. The alternating direction implicit (ADI) method is employed for an efficient simulation of the model. It has been numerically verified that the resulting algorithm can form clear edges in 2 to 3 AD! iterations. Various results are given to show th eeffectiveness and reliability of the algorithm. [ABSTRACT FROM AUTHOR]

Frame rate up conversion (FRUC) methods that em- ploy motion have been proven to provide better image quality compared to nonmotion-based methods. While motion-based methods improve the quality of interpolation, artifacts are introduced in the presence of incorrect motion vectors. In this paper, we study the design problem of optimal temporal interpolation filter for motion-compensated FRUC (MC-FRUC). The optimal filter is obtained by minimizing the prediction error variance between the original frame and the interpolated frame. In FRUC applications, the original frame that is skipped is not available at the decoder, so models for the power spectral density of the original signal and prediction error are used to formulate the problem. The closed-form solution for the filter is obtained by Lagrange multipliers and statistical motion vector error modeling. The effect of motion vector errors on resulting optimal filters and prediction error is analyzed. The performance of the optimal filter is compared to nonadaptive temporal averaging filters by using two different motion vector relliability measures. The results confirm that to improve the quality of temporal interpolation in MC, the interpolation filter should be designed based on the reliability of motion vectors and the statistics of the MC prediction error. [ABSTRACT FROM AUTHOR]

Changing resolution of images is a common operation. It is also common to use simple, i.e., small, interpolation kernels satisfying some "smoothness" qualities that are determined in the spatial domain. Typical applications use linear interpolation or piecewise cubic interpolation. These are popular since the interpolation kernels are small and the results are acceptable. However, since the interpolation kernel, i.e., impulse response, has a finite and small length, the frequency domain characteristics are not good. Therefore, when we enlarge the image by a rational factor of (L/M), two effects usually appear and cause a noticeable degradation in the quality of the image. The first is jagged edges and the second is low-frequency modulation of high-frequency components, such as sampling noise. Both effects result from aliasing. Enlarging an image by a factor of (L/M) is represented by first interpolating the image on a grid L times finer than the original sampling grid, and then resampling it every M grid points. While the usual treatment of the aliasing created by the resampling operation is aimed toward improving the interpolation filter in the frequency domain, this paper suggests reducing the aliasing effects using a polyphase representation of the interpolation process and treating the polyphase filters separately. The suggested procedure is simple. A considerable reduction in the aliasing effects is obtained for a small interpolation kernel size. We discuss separable interpolation and so the analysis is conducted for the one-dimensional case. [ABSTRACT FROM AUTHOR]

A cost-effective digital camera uses a single-image sensor, applying alternating patterns of red, green, and blue color filters to each pixel location. A way to reconstruct a full three-color representation of color images by estimating the missing pixel components in each color plane is called a demosaicing algorithm. This paper presents three inherent problems often associated with demosaicing algorithms that incorporate two-dimensional (2-D) directional interpolation: misguidance color artifacts, interpolation color artifacts, and aliasing. The level of misguidance color artifacts present in two images can be compared using metric neighborhood modeling. The proposed demosaicing algorithm estimates missing pixels by interpolating in the direction with fewer color artifacts. The aliasing problem is addressed by applying filterbank techniques to 2-D directional interpolation. The interpolation artifacts are reduced using a nonlinear iterative procedure. Experimental results using digital images confirm the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

Every now and then, a new design oran interpolation kernel shows up in the literature. While interesting results have emerged, the traditional design methodology proves laborious and is riddled with very large systems of linear equations that must be solved analytically. In this paper, we propose to ease this burden by providing an explicit formula that will generate every possible piecewise-polynomial kernel given its degree, its support, its regularity, and its order of approximation. This formula contains a set of coefficients that can be chosen freely and do not interfere with the four main design parameters; it is thus easy to tune the design to achieve any additional constraints that the designer may care for. [ABSTRACT FROM AUTHOR]