nils-herrmann/clean_scirepeval huggingface dataset:
Linear-phase paraunitary filter banks with unequal length

We present a design and implementation of M-channel linear-phase paraunitary filter banks (LPPUFB) with unequal length filters and same center points. Our main motivation is the application in effective image coding. For low-frequency signals, long basis functions should be used to avoid the blocking effect. While, short basis functions should be used to reduce the ringing noise for high-frequency signals. LPPUFB with such characteristics can be achieved structurally by taking account of the lattice structure. Finally some design examples are shown.
Simplified realization of normalized integer WHT for multiplierless integer DCT

Walsh-Hadamard transform (WHT) based multiplierless integer discrete cosine transform (IntDCT) has structural regularity even in short word length lifting coefficients. It, however, cannot apply to image coding without the quantization part because WHT was implemented by only ±1 adder operations without the normalization scaling factors. Although we have already presented a normalized integer WHT (IntWHT) as its solution, it also has many adder operations. In this paper, using a two-dimensional (2-D) separable transform of one-dimensional (1-D) normalized WHT is applied to each lifting coefficient, we present a more simplified realization of normalized IntWHT with structural regularity for short word length lifting coefficients. Finally, in lossless-to-lossy image coding, IntDCT based on the proposed IntWHT is validated.
Design of Block Lifting-Based Discrete Cosine Transform Type-II and IV

This paper presents a novel lifting factorization of discrete cosine transform type-II and IV (DCT-II and IV). Although some conventional integer DCT-IIs (IntDCT-IIs) with block size 8 have been proposed, they are not generalized as arbitrary block size M. Using block lifting factorization which has an efficient structure for lossless-to-lossy image coding, we present IntDCT-IIs and IVs with arbitrary block size M that is called block lifting-based DCT-IIs and IVs (BLDCT-IIs and IVs). Finally, the validity of our method is proved by showing the results of lossless-to-lossy image coding in the most general case of the block size 8 and the extended size 16.
Image super-resolution based on shock filter and non-local means

In this paper, we propose a super-resolution method based on the shock filter and non-local means. The super-resolution generates the enlarged images which have sharp edges, clear characters and fine textures. The proposed method gradually improves the sharpness of contours by the shock filter and effectively maintains the structures and textures by the nonlocal means. In some simulations, the proposed method shows better results than conventional ones and comparable results to the state-of-the-art super-resolution method which requires high computational complexities. The proposed method requires low computational complexities, because it can be realized by simple iterative processes.
Image restoration based on weighted average of multiple blurred and noisy images

In this paper, we propose a new method for image restoration from a pair of images with different noise and blur artifacts. These images are obtained from the camera with different exposure time and the restored images have higher quality. Some restoration methods using multiple degraded images have been proposed. Most of these methods solve the optimization problem achieving the noise removal and the blur suppression at the same time. However, this approach cannot handle the degree of noise removal and blur suppression easily. This paper proposes a new method for image restoration from a pair of images with different noise and blur artifacts. We take a wighted average of the two images to produce one image for the restoration process. By merging the noisy image, the noise and blur artifact can be efficiently suppressed while keeping useful image information. Then we propose a simple restoration method and obtain a higher quality restored image. Experiment results show that the proposed method can obtain a higher quality restored images which are removed noise and preserved edges.
Realization of lossless-to-lossy image coding compatible with JPEG standard by direct-lifting of DCT-IDCT

A discrete cosine transform (DCT) can be easily implemented in software and hardware for the JPEG and MPEG formats. Recently, some integer DCTs (IntDCTs) for lossless-to-lossy image coding have been proposed, but they do not satisfy enough compatibility with JPEG standard. This paper proposes a realization of lossless-to-lossy image coding which has higher compatibility with it than the conventional IntDCTs. Our IntDCT is implemented by direct-lifting of DCT and inverse DCT (IDCT) and has high performance in lossless image coding compared with any IntDCT while keeping high compatibility with JPEG standard. Finally, our method is validated by its application to lossless-to-lossy image coding.
Design of linear phase M-channel perfect reconstruction FIR filter banks

We propose two approaches to design channel nonpa- raunitary filter banks that satisfy perfect reconstruction (PR) and linear phase (LP) properties. In the first approach, the PR condition is imposed on only a highpass filter. Although this method does not require nonlinear optimization, it has a demerit in that the order of a highpass filter becomes high. In the second approach, two filters are optimized simultaneously using a Lagrange-Newton method. We can design PR filter banks that have the same length. The PR constraint is also formulated as a linear and nonlinear equation of the analysis filter coefficients. Finally, some design examples are included.
Design of complex all-pass networks using Remez algorithm

A new method for designing complex all-pass digital filters is introduced. Phase error is regarded as the amplitude of complex error between the designed and the desired all-pass function. Then the Remez exchange algorithm is applied to the amplitude of complex error, and it is approximated to be equiripple. Although equiripple solutions to phase approximation problems are not necessarily optimum in the Chebyshev sense, considerable design experience shows that equiripple approximations are often quite satisfactory results. Such cases are considered, and it is shown that the best uniform phase approximation to an arbitrarily specified phase response can be found. In this algorithm, a satisfactory solution is obtained through a few iterations without any initial guess of the solution. Furthermore, as one of the complex all-pass digital filter applications, a large class of real coefficient doubly complementary IIR digital filter pairs is introduced by using a single complex all-pass digital filter, which has approximately linear phase.
Integer DCT Based on Direct-Lifting of DCT-IDCT for Lossless-to-Lossy Image Coding

A discrete cosine transform (DCT) can be easily implemented in software and hardware for the JPEG and MPEG formats. However, even though some integer DCTs (IntDCTs) for lossless-to-lossy image coding have been proposed, such transform requires redesigned devices. This paper proposes a hardware-friendly IntDCT that can be applied to both lossless and lossy coding. Our IntDCT is implemented by direct-lifting of DCT and inverse DCT (IDCT). Consequently, any existing DCT device can be directly applied to every lifting block. Although our method requires a small side information block (SIB), it is validated by its application to lossless-to-lossy image coding.
Wavelet-based content-aware image coding with rate-dependent seam carving

This paper proposes an image coding method incorporated with content-aware image resizing. It is a promising application of our previously proposed rate-dependent seam carving (RD-SC). RD-SC is combined with lifting-based discrete wavelet transform to construct a new multiresolution image representation. Furthermore, the modified parent-children relationship of SPIHT is shown. Our proposed image coding presents comparable performances to the conventional SPIHT even for the original size, and retargeted image quality is satisfactory.
A novel design of criticially sampled contourlet transform and its application to image coding

In this paper, a novel design method of critically sampled contourlet transform (CSCT) is proposed. Although, several types of CSCT have been proposed, they have some problems on efficiency and flexibility of their frequency plane partition patterns. In contrast to the way in conventional design methods based on a "top-down" approach, the proposed one is based on a "bottom-up" one. That is, the proposed CSCT decomposes the frequency plane into small directional sub- bands, and then synthesizes them up to a target frequency plane partition, while the conventional ones decompose into it directly. By this way, the proposed CSCT can provide an efficient and flexible frequency plane partition for image coding.
Random-valued impulse noise removal using non-local search for similar structures and sparse representation

In this paper, we introduce a new method to remove random-valued impulse noise in an image. Random-valued impulse noise replaces the pixel value at a random position by a random value. Due to the randomness of the noisy pixel values, it is difficult to detect them by comparison with neighboring pixels, which is used in many conventional methods. Then we improve the recent noise detector which uses a non-local search of similar structure. Next we propose a new noise removal algorithm by sparse representation using DCT basis. Furthermore, the sparse representation can remove impulse noise by using the neighboring similar image patch. This method has much more superior noise removal performance than conventional methods.
Simplified DCT-lifting-based reversible lapped transforms using parallel processing of two same type lapped transforms

We present a realization of reversible lapped transforms (RevLTs) with simplified implementations, which are constructed by DCT and DST matrices, adders, and bit-shifters, for lossy-to-lossless image coding in this paper. Each DCT or DST matrix is directly used to each lifting coefficient block and it is called DCT-lifting structure. The structure is obtained by considering parallel processing of two `same' type LTs and using DCT-lifting factorizations as our previous work. Furthermore, the Hadamard transform and scaling parts in the RevLTs are effectively implemented by extending 2D non-separable lifting structures derived from lifting-based lapped transform (L-LT) used for JPEG XR, the newest image coding standard. As a result, the proposed RevLTs achieve not only simplified implementations with any block size, but also comparable lossy-to-lossless image coding performance to the conventional RevLTs.
Multipitch estimation and instrument recognition by exemplar-based sparse representation

This paper investigates the pitch estimation and the instrument recognition of music signals. A note exemplar is a spectrum segment of notes of the specific pitch and instrument, which is stored as a form of dictionary preliminarily. We describe the method of reconstructing a frame of musical signals as the linear combination of exemplars from the large exemplar dictionary with sparse (l1 minimized) coefficient vector. Reconstruction constraints are imposed to KL divergence of spectra, which is found to produce better results than Euclidean distance. The proposed algorithm shows the ability to transcript music pieces with relatively many notes per a frame and to divide the instrument explicitly through some experiments.
Image super-resolution method based on non-local means and self similarity

In this paper, we propose an image super-resolution method based on the non-local means and the self similarity. Various super-resolution methods can correctly estimate the missing high frequency components of enlarged images. However, they mostly require high computational costs, which is not suitable for real-time processing. For a super-resolution with low computational costs, the proposed method is simply realized via the block matching technique with a small search area. Since it utilizes the image self similarity and sparsity, it produces visually efficient interpolated images. In the simulation, it is shown that the proposed method greatly outperforms the bicubic in a visual quality of enlarged images, objectively and perceptually.
Design of 2-dimensional perfect reconstruction filter banks for arbitrary sampling lattices

A design method of 2-dimensional perfect reconstruction filter banks for arbitrary sampling lattices is presented. Filter banks are constructed by a 2D nth band filter which consists of some delays and 2D interpolated all-pass networks. It is shown that the analysis and synthesis part is constructed by combining the polyphase structure of a 2D Nth-band digital filter and 2D discrete Fourier transform (DFT).<<ETX>>
Design of directional filters using downsampling

We present a design method of a directionally oriented 2-D filter (directional filters) and filter bank. At first, we consider the 2-D prototype passband filter. Next, using downsampling, we decimate the prototype filter coefficient and design the directional filters. The passband regions of the component filters are wedge shaped and thus provide the directional information. By using this method, we can reduce the number of filters which we must design. Finally, we show the 2-D filter bank structure with above-mentioned filter.
An Efficient Lifting Structure of Biorthogonal Filter Banks for Lossless Image Coding

This paper introduces an image transform method by using M-channel biorthogonal filter banks (BOFBs) with an efficient lifting factorization. The proposed lifting factorization of a building block in their lattice structure has unity diagonal scaling coefficients and guarantees perfect reconstruction even if the obtained coefficients are quantized. Since the number of rounding operators of proposed lifting-based BOFBs (LBBOFBs) can be reduced by merging the lifting steps, the proposed structure is efficient for lossless image coding. Image coding results indicate better performance than conventional methods.
Digital compensation scheme for coefficient errors of complex filter bank parallel A/D converter in low-IF receivers

A digital compensation scheme for coefficient errors of a complex filter bank in low-IF receivers is presented. The complex filter bank is employed to suppress DC offset and image signals in the low-IF receivers and relax the requirements on the conversion rate and resolution of A/D converters. The proposed compensation scheme regenerates interference due to the coefficient errors and subtracts it from the digital signal converted by an A/D converter. The proposed scheme also improves the effective resolution of A/D converters.
Theory and Design of Two-Channel Complex Linear-Phase Pseudo-Orthogonal Filterbanks

In recent years, two-channel complex-valued filterbanks have been studied and found theirs several important applications by many researchers, such as complex signal and image processing. One of those important results is that there is no two-channel complex-valued linear-phase paraunitary filterbank (CLPPUFB), except for its filter lengths of 2. In this paper, we introduce a class of special complex-valued filterbanks which is a subclass of biorthogonal filterbanks. Those filterbanks are called complex pseudo-orthogonal filterbanks (CPOFB), based on the concept of pseudo-orthogonality (PO). This kind of orthogonality, we propose, is different from the conventional one essentially. This paper also shows possibility to design a two-channel complex linear-phase filterbank (CLPFB) with its filter lengths are more than 2, based on PO. Finally, we show a design example of a complex linear-phase pseudo-orthogonal filterbank (CLPPOFB). Moreover, such CPOFB can satisfy the linear-phase condition simultaneously with filter lengths are more than 2. This paper shows a theory, a design method and an example of CLPPOFB
HMM-based surface reconstruction from single images

In this paper, a novel method of the surface reconstruction from a single monocular image is proposed. Our proposed approach (called shape from knowledge) is based on the knowledge of objects, which is acquired by learning from a number of samples. To achieve this, we investigate making use of the hidden Markov model (HMM) framework, which models the correspondence between an intensity image an its depth information. We have applied our algorithm to the 3-D face and 3-D hand reconstruction from single images, and the results show the effectiveness of the proposed method.
Seam merging for image resizing with structure preservation

In image resizing process, preserving structure on an image is important to produce plausible results. We proposed seam merging, a seam-carving-based image resizing method, and a new energy criterion for structure preservation. Seam merging reduces the size of an image by merging a two-pixel-width seam into a one-pixel width seam. To find the optimal seam that minimizes structural distortion after merging, we introduce an energy function which has a large value when the merging breaks the structure. We further show the way to find the optimal seam using dynamic programming. Experimental results demonstrate that our proposal algorithm can produce fewer artifacts and successfully preserve structure.
Sparsity Constrained Mixture Modeling for the Estimation of Kinetic Parameters in Dynamic PET

The estimation and analysis of kinetic parameters in dynamic positron emission tomography (PET) is frequently confounded by tissue heterogeneity and partial volume effects. We propose a new constrained model of dynamic PET to address these limitations. The proposed formulation incorporates an explicit mixture model in which each image voxel is represented as a mixture of different pure tissue types with distinct temporal dynamics. We use Cramér-Rao lower bounds to demonstrate that the use of prior information is important to stabilize parameter estimation with this model. As a result, we propose a constrained formulation of the estimation problem that we solve using a two-stage algorithm. In the first stage, a sparse signal processing method is applied to estimate the rate parameters for the different tissue compartments from the noisy PET time series. In the second stage, tissue fractions and the linear parameters of different time activity curves are estimated using a combination of spatial-regularity and fractional mixture constraints. A block coordinate descent algorithm is combined with a manifold search to robustly estimate these parameters. The method is evaluated with both simulated and experimental dynamic PET data.
Impatient MRI: Illinois Massively Parallel Acceleration Toolkit for image reconstruction with enhanced throughput in MRI

Much progress has been made in the design of efficient acquisition trajectories for high spatial and temporal resolution in magnetic resonance imaging (MRI). Additionally, significant developments in image reconstruction have enabled the reconstruction of reasonable images from massively undersampled or noisy data that is corrupted by a variety of physical effects, including magnetic field inhomogeneity. Translation of these techniques into clinical imaging has been impeded by the need for expertise and computational facilities to realize the potential of these methods. We present the Illinois Massively Parallel Acceleration Toolkit for Image reconstruction with ENhanced Throughput in MRI (IMPATIENT MRI), a reconstruction utility that enables advanced techniques within clinically relevant computation times by using the computational power available in low-cost graphics processing cards.
Reduced-encoding MRI using higher-order generalized series

Reduced-encoding MRI has been used in a wide variety of MR applications where temporal resolution is critical. Although the Generalized Series model (with basis functions constructed from a reference image) allows the reconstruction of high-resolution dynamic images from a small number of encodings, the ability of the model to capture localized dynamic features is limited by the model order, which in the past has been set equal to the number of encodings acquired. This paper extends this model by incorporating higher frequency terms, which allows for a sharper reconstruction of new localized features. Since the series coefficients of the higher-order model are underdetermined by the data collected, two important issues arise which are addressed in this paper: the definition of an appropriate regularization criterion and the solution of the corresponding optimization problem. Results from simulated as well as biological data are also provided to demonstrate the properties of this model
Further development in anatomically constrained MR image reconstruction: Application to multimodal imaging of mouse stroke

MR imaging can leverage a wide variety of intrinsic contrast mechanisms to provide detailed information regarding the anatomy, function, physiology, and metabolism of biological tissues. However, because of low sensitivity, many experiments that reveal higher-order structure and function have been limited due to inherent trade-offs between data acquisition time, signal-to-noise ratio, and resolution. This paper describes the further development of a statistical framework for MR image reconstruction which helps to mitigate these effects. Specifically, we advocate the collection of high-resolution multi-modal MR imaging data, and utilize the correlation between features in these different images to reduce noise while maintaining resolution. The proposed approach is illustrated with joint reconstruction of relaxometry and spectroscopic imaging data in a mouse model of stroke.
Navigator-Free EPI Ghost Correction With Structured Low-Rank Matrix Models: New Theory and Methods

Structured low-rank matrix models have previously been introduced to enable calibrationless MR image reconstruction from sub-Nyquist data, and such ideas have recently been extended to enable navigator-free echo-planar imaging (EPI) ghost correction. This paper presents a novel theoretical analysis which shows that, because of uniform subsampling, the structured low-rank matrix optimization problems for EPI data will always have either undesirable or non-unique solutions in the absence of additional constraints. This theory leads us to recommend and investigate problem formulations for navigator-free EPI that incorporate side information from either image-domain or k-space domain parallel imaging methods. The importance of using nonconvex low-rank matrix regularization is also identified. We demonstrate using phantom and in vivo data that the proposed methods are able to eliminate ghost artifacts for several navigator-free EPI acquisition schemes, obtaining better performance in comparison with the state-of-the-art methods across a range of different scenarios. Results are shown for both single-channel acquisition and highly accelerated multi-channel acquisition.
Joint reconstruction of noisy high-resolution MR image sequences

Quantitative MR studies often utilize sequences of coregistered images, where the contrast in each image frame is experimentally manipulated to enable the regression of important physical parameters. However, the potential of these experiments has been limited for high-resolution biological studies because of long acquisition times and limited signal-to- noise ratio. This work presents a new approach for the reconstruction of an image sequence from noisy data, using a statistical model that incorporates an implicit line-site prior to take advantage of the high level of inter-frame correlation between spatial image features. Reconstructions are efficiently computed using a globally-convergent half-quadratic iterative algorithm, and the proposed optimization procedure enables precise characterization of resolution and noise properties.