nils-herrmann/clean_scirepeval huggingface dataset:
Electron beam pumped argon-excimer laser using an unstable resonator

An unstable resonator was used for an e-beam pumped argon-excimer laser in the VUV-spectral region, for the first time to our knowledge. Adoption of the unstable resonator achieved successful operations of the high output energy of 28 mJ without any damage of the resonator mirrors. Beam divergence of the argon-excimer laser output was measured with a beam profiler, which was developed in our laboratory, It was found that the output beam involved a large ASE output. The maximum focused fluence at the center of the output beam was measured to be 42 mJ/cm/sup 2/. The coherence of the argon-excimer laser was degraded by the ASE, which was caused due to high gain condition of the argon-excimer laser.
Local Spectral Component Decomposition for Multi-Channel Image Denoising

We propose a method for local spectral component decomposition based on the line feature of local distribution. Our aim is to reduce noise on multi-channel images by exploiting the linear correlation in the spectral domain of a local region. We first calculate a linear feature over the spectral components of an M-channel image, which we call the spectral line, and then, using the line, we decompose the image into three components: a single M-channel image and two gray-scale images. By virtue of the decomposition, the noise is concentrated on the two images, and thus our algorithm needs to denoise only the two gray-scale images, regardless of the number of the channels. As a result, image deterioration due to the imbalance of the spectral component correlation can be avoided. The experiment shows that our method improves image quality with less deterioration while preserving vivid contrast. Our method is especially effective for hyperspectral images. The experimental results demonstrate that our proposed method can compete with the other state-of-the-art denoising methods.
Design of causal IIR filter banks satisfying perfect reconstruction

In this paper we present design methods for IIR filter banks. In the conventional works for IIR filter bank systems, the stable and causal systems which satisfy the condition of perfect reconstruction have not been well established yet. Most of these works do not satisfy both of these conditions (causality and perfect reconstruction). In this study, to design a two channel IIR filter bank system which satisfies the above two properties, we propose new design algorithms based on a Lagrange-multiplier method and Lagrange-Newton method. In this method the system achieves perfect reconstruction and has causality.
Joint Sparsity and Order Optimization Based on ADMM With Non-Uniform Group Hard Thresholding

This paper proposes a new optimization framework for the joint optimization of sparsity and filter order (JOSFO) for FIR filter design. Since the cost function for JOSFO involves <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula> and non-uniform overlapped group <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula> norms, which are not convex, a global optimal solution is difficult to obtain. To find an approximate solution of the non-convex problem, existing approaches repeat the following steps: 1) approximate the cost function; 2) find candidates of zero coefficients by minimizing the cost function; and 3) set them to zero. On the other hand, this paper directly solves the optimization problem, without any approximation to the cost function, by using the alternating direction method of multipliers with the pseudo-proximity operators of <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula> and non-uniform non-overlapped group <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula> norms. Experimental results show that resulting filters designed by the proposed method have sparser coefficients and lower orders, while satisfying filter specifications, such as an error from a desired frequency response.
High dynamic range image acquisition using flash image

We propose a denoising technique using multiple exposure image integration. When acquiring a dark scene, the detail of the dark area is often deteriorated by sensor noise. For a high dynamic range image acquisition, denoising in dark areas is a critical issue, since the dark area is, in general, enhanced by a tone-mapping and the noise is made more visible when displaying it on an output devise. In our method, a flash image is utilized as well as no-flash multiple exposure images to further reduce the noise. Multiple exposure integration is performed in a wavelet domain, where noise removal is achieved by the wavelet-shrinkage for multiple exposures. Our method works well especially for noise in shadows. We show the validity of the proposed algorithm by simulating the method with some actual noisy images.
Multiple exposure integration for restoring all in-focus images

When taking a photograph of a high dynamic range scene, saturation of pixel values (that is under/over-exposure) often occurs due to the narrower dynamic ranges of commercial cameras. It is possible to solve the problem of the under/over-exposure by generating High Dynamic Range (HDR) Images. The HDR image is generated by integration of multiple exposure images that are taken with different exposure settings. When taking multiple exposure images for the scene that contains dark areas, one often copes it by adjusting one of the three settings: (1) camera's sensitivity, (2) exposure time, (3) lens' aperture, in order to compensate shortage of the quantity of light. Each of the three methods has a drawback. The high camera sensitivity enhances noises as well as signals. The long exposure causes motion blur. The lens with wide aperture yields out-of-focus images. The first two problems can be solved by denoising and deblurring, respectively, and there are many methods for solving the problems. Our aim is to address the third problem. We generate an all in-focus image without the under/over-exposure and out-of-focus areas due to the wide aperture by integrating the multiple exposure images. We introduce a new technique for image integration which simultaneously addresses the problems of the under/over-exposure and defocus. The validity of the proposal technique is shown by comparing it with several conventional methods for real scenes.
Higher degree FIR digital differentiators based on Taylor series

Simplified designs having closed form expressions for impulse response coefficients of maximally linear FIR digital differentiators are already available. We extend these designs to higher degree differentiators and present closed form expressions for their coefficients. The designs are in fact solutions of systems of linear approximate equations based on the Taylor series, formally known as central difference approximations. We avoid this computationally expensive step and present closed form expressions for the solution, which can be implemented as digital differentiators of arbitrary order and degree, and have maximal accuracy at /spl omega/ = 0. We also present as new class of central difference approximations, and find explicit formulas for their coefficients. Differentiators of odd degree based on these new approximations are more accurate in the upper frequency bands.
Image coding with face descriptors embedding

Content descriptors, useful for browsing and retrieval tasks, are generally extracted and treated as a separate entity with respect to the nature of the content itself. At the same time, conventional coding processes do not take into account information carried out by content descriptors. Content descriptors are closely related to the content itself, and they potentially can be used to exploit redundancy in entropy coding processes. Embedding content descriptors in the bitstream can reduce content description extraction load, and at the same time, it can reduce the rate associated to the compressed content and its description. In this paper an effective implementation of this approach is presented, where image descriptors are actively used in the coding process for exploiting redundancy. First of all, image areas containing faces are detected and encoded using a scalable method, where the base layer is represented by the corresponding eigenface, and the enhancement layer is formed by the prediction error. The remaining areas are then encoded by using a traditional approach. Simulations show that achievable compression performances are comparable with those provided by conventional, making the proposed approach very convenient for source coding and content description.
New Designs of MAXFLAT FIR Halfband Low/High Pass Digital Filters with Narrow Transition Bands

MAXFLAT FIR digital filters has the smoothest and the most accurate magnitude responses among all the available types of FIR digital filters. However, the transition bands of MAXFLAT filters are relatively wider, and this makes them unattractive in certain applications. Traditionally, low/high pass MAXFLAT FIR filters are designed to satisfy MAXFLAT constraints at ends of the frequency band. In this paper, we present two new designs of halfband low/high pass filters that satisfy these constraints at the middle of the pass and stop bands. The first design is obtained by solving a system of linear equations obtained by applying MAXFLAT constraints to the magnitude response of the filter, while the second design is a transformation of one of our previous designs of maximally linear digital differentiators. Design examples show that the transition bands of the presented designs are narrower as compared to traditional designs
High Dynamic Range Image Watermarking Robust Against Tone-Mapping Operators

High dynamic range (HDR) images represent the future format for digital images since they allow accurate rendering of a wider range of luminance values. However, today special types of preprocessing, collectively known as tone-mapping (TM) operators, are needed to adapt HDR images to currently existing displays. Tone-mapped images, although of reduced dynamic range, have nonetheless high quality and hence retain some commercial value. In this paper, we propose a solution to the problem of HDR image watermarking, e.g., for copyright embedding, that should survive TM. Therefore, the requirements imposed on the watermark encompass imperceptibility, a certain degree of security, and robustness to TM operators. The proposed watermarking system belongs to the blind, detectable category; it is based on the quantization index modulation (QIM) paradigm and employs higher order statistics as a feature. Experimental analysis shows positive results and demonstrates the system effectiveness with current state-of-art TM algorithms.
Range image registration using multiresolution decomposition

This paper describes fast registration approaches of range images based on their multiresolution representations. In the iterative closest point algorithm, which is a popular technique to register sets of 3D points, the computational complexity depends largely on the number of points representing range image. Therefore, we accelerate a range image registration by applying shape-adaptive discrete wavelet transform to range images and efficiently reducing the number of data points. In addition, we present a fast and accurate registration method, which finds the closest point to a given point hierarchically in consideration of the wavelet decomposition. Finally, we apply these techniques to the registration of actual range images and show the validity of the proposed approaches.
Hierarchical human motion compression with constraints on frames

This paper presents a compression method for motion data with special characteristics which can be indicated by motion behavior or specified by user with constraint. Motion capture systems have been widely used to model motion behavior in feature film production, action games and virtual environments on network. In general, the motion data consist of three coordinates or some rotation angles in each frame. For storing motion data and real-time transmission, an uncompressed raw data is often unacceptable. Our method combines wavelet transform and kinematics to implement an efficient motion data compression. Controlling the distortion yielded by the quantization, we enable user to specify constrains of motion. The max shift ROI method is adopted to sign the constraint frames. We further apply an adaptive quantization to establish the optimal position of the joint. Our method achieves 5-20% compression without any visual artifacts. The experiment result shows the validity of our method.
Area-Distribution Uniformizing Parameterization for Preserving Features of 3D Meshes

3D mesh parameterization which converts the complicated 3D mesh to the flat and non-overlapped 2D mesh is used for "texture-mapping" in order to make the correspondence between a texture-image and a 3D mesh in 2D space, and "remeshing" in order to convert polygonal meshes to the regular grid meshes equipped with customizability. In this paper, we propose a 3D mesh parameterization method which is able to express more detailed shape of the 3D model. However, this one-sided emphasis on "remeshing" incurs texture-distortions in practice. So, we also propose a texture-mapping method which uses the transform-map of texture-coordinates to keep "texture-mapping" qualities
Quantitative evaluation method of visual difference caused by image decimation

This paper proposes a method for evaluating visual difference caused by decimation. In many applications it is important to evaluate visual difference of two different images. There exist many image assessment methods that utilize the human visual system (HVS), such as the visual difference predictor (VDP) and the Sarnoff visual discrimination model. In this paper, we extend and elaborate on the conventional image assessment method for the purpose of evaluating the visual difference caused by the image decimation. Our method matches actual human evaluation more and requires less computation complexity than the conventional method.
Detail preserving multiple bit-depth image representation and coding

Recent trends in high bit depth representation of images, including high dynamic range images, increases demand for multiple bit depth representation. When reducing the bit depth, detailed variations in the original image are lost through quantization. We propose a scheme for quantization using the bilateral based detail preservation. Our approach has an ability to precisely restore the original bit depth from the quantized image. We apply the multiple bit-depth representation for the image coding and show the validity of our method. We demonstrate the effectiveness of our approach through some experiences.
New designs of frequency selective FIR digital filters

Fourier series expansion is used to explore the relationship between the coefficients of halfband lowpass and general bandpass FIR digital filters. This relationship is used to derive new designs of band pass/stop and low/high pass MAXFLAT FIR digital filters, from an available design of halfband lowpass MAXFLAT FIR digital filters. The presented designs have explicit formulas for the filter coefficients, and can give infinite number of distinct filters of any finite length. The formulas are modified to obtain a new class of filters, which allow a reduction in the transition bandwidth at the expense of smoothness of the magnitude response, and have a performance comparable to that of the minimax filters.
Advanced weighting scheme in the rational Remez algorithm for IIR digital filters

In this paper, we present a numerical method for the equiripple approximation of IIR digital filters. Many methods have been proposed for the design of such filters. One of the most practical methods is the rational Remez algorithm, in which the squared magnitude response of the IIR digital filter is approximated by solving an eigenvalue problem, and then the filter is obtained by factorizing it. Unlike the original Remez algorithm for FIR filters, it is difficult for the rational Remez algorithm to control the ratio of ripples between different bands. In the conventional lowpass filter design, for example, when different weights are given for its passband and stopband, one needs to iteratively design the filter by trial and error to achieve the ratio of the weights exactly. To address this problem, we modify the conventional algorithm and make it possible to directly control the ripple ratios. The method iteratively solves eigenvalue problems with controlling the ratio of ripples. Using this method, the equiripple solutions are obtained quickly.
New local tone mapping and two-layer coding for HDR images

This paper proposes a two-layer High Dynamic Range (HDR) coding scheme using a new tone mapping. Our tone mapping method transforms an HDR image onto a Low Dynamic Range (LDR) image by using a base map that is a smoothed version of the HDR luminance. In our scheme, the HDR image can be reconstructed from the tone mapped LDR image. Our method makes use of this property to realize a two-layer HDR coding by encoding both of the tone mapped LDR image and the base map. This paper validates its effectiveness of our approach through some experiments.
Measuring error on 3D meshes using pixel division

In general, highly detailed 3D meshes are often large and time-consuming to download. In order to address the problem, the amount of 3D data is reduced by decimating triangles which has little influence on visual quality. However, since few schemes for quantitative evaluation have been established yet, subjective evaluation is often used. While 1D and 2D digital signals exist only on sampling points, there are no "sampling points" in 3D meshes which makes it difficult to evaluate the differences of two meshes. A computational scheme to measure the error between two meshes is proposed. The error is measured by dividing 3D meshes into some 2D planes and then further segmenting the 2D planes to pixels. Our algorithm handles not only the geometric error but also color differences.
Level of Detail Control for Texture on 3D Maps

This paper proposes a method to control texture resolution levels for large-scale 3D maps of urban areas. Recently, performance of computers has been developed rapidly, but it is still hard to directly handle the large-scale 3D maps on PCs, because of its high requirement for hardware capability. To render it, the 3D data reduction is needed during rendering process. On the 3D maps, texture data, in general, tend to be far larger than geometry data. So reduction of rendering texture data by using the LOD (level of detail) is effective to reduce whole data size. To achieve that, we propose a method to control the resolution level of texture. In this method, appropriate texture levels are decided offline according to their rendered sizes on a display and importance of the texture images. This determination is done in advance to reduce processing load during rendering process
Design of FIR filters with decimated impulse responses

In this paper, we present a numerical algorithm for the design of FIR filters with sparse impulse responses. Our method minimizes the number of nonzero entries in the impulse response together with the least squares error of its frequency response. We show that the FIR filters with sparse coefficients can outperform a conventional least squares approach and the Parks-McCllelan method under the condition of the same number of multipliers.
Wavelet packet algorithm for quadtree-based embedded image coding

Wavelet packets represent an elegant generalization of the wavelet transform, capable of providing arbitrary frequency resolution to meet a signal's spectral behaviors. Recent research advances have shown that wavelet-based image compression techniques offer several advantages over traditional techniques in terms of progressive transmission capability and compression efficiency. The embedded compression techniques - tree-structured coding techniques, such as embedded zerotree wavelet (EZW) and set partitioning in hierarchical trees (SPIRT), and quadtree-structured coding techniques, such as wavelet quadtree (WQT), multigrid embedded (MGE) and EZW based on intraband partitioning (EZW-IP), - demonstrate the competitive performance of wavelet based compression schemes. This paper describes the wavelet packet algorithm which makes it suited to subsequent quad tree-based embedded image coders, such as EZW-IP. In our technique, the hierarchical sums of maximum magnitude of each quadtree node role as the decomposition criterion. The simulation results for various images show that the proposed algorithm gives a better coding efficiency than the octave-band decomposition.
Watermarking for Progressive Human Motion Animation

In this paper, we present a method for a motion capture data watermarking. The digital watermarking is commonly used for copyright protection by embedding the information identifying the owner in the data. The embedded watermark should be imperceptible and robust. On the other hand, the key-framing method for progressive encoding the motion data gives the frames an order. This order is resilient to the motion retrieval. With this order, we can embed a watermark into a characteristic of the frames. We also present the method of detecting the watermark by analyzing the distribution of the suspected signal. Moreover, our watermarking scheme is a progressive watermarking, as more and more frames are sent, the confidence of the watermark increases. Experimental results show that for various motion data, our watermarking is imperceptible and robust to some possible attacks.
A regularization approach for bayer reconstruction in lossy image coding by inverse demosaicing

Color image coding by inverse demosaicing, which exploits the implicit Bayer structure in color images, has a potential to achieve superior performance compared to conventional color image coding methods. The previous framework of inverse demosaicing was limited to lossless and near-lossless data compression, while this paper explores its adaptation to lossy compression. To cope with distortions due to lossy compression, we propose a regularization approach using side color information for the Bayer recovery problem in the decoder. Thanks to careful design of the regularization, the resulting Bayer recovery problem becomes an unconstrained quadratic programming problem, and thus several efficient solvers can be used. A numerical example demonstrates the efficacy of our approach. It can significantly reduce distortions in the recovery of the Bayer data and keep the total bit rate.
Narrow Transition Band FIR Hilbert Transformers With Flat Magnitude Response

Maximally flat (MAXFLAT) finite-impulse response digital filters, including the Hilbert transformers (HTs), have the smoothest magnitude responses among the available types of FIR digital filters. However, the transition bands of MAXFLAT filters are relatively wider, and this makes them unattractive in certain applications. We present new designs of even and odd length HTs by transforming a design of digital differentiators satisfying maximal linearity constraints at omega = pi/2. The resultant even and odd length HTs have highly smooth magnitude responses around omega = pi/2 and omega = (pi/4,3pi/4) respectively, and have relatively narrow transition bands compared to the existing MAXFLAT designs.