nils-herrmann/clean_scirepeval huggingface dataset:
Hyperspectral image restoration based on spatio-spectral structure tensor regularization

We propose a regularization function for hyperspectral image restoration based on a newly-designed structure tensor. We adopt a convex optimization approach with the use of the nuclear norm of a matrix, termed as spatio-spectral structure tensor. It consists of the gradient components of a hyperspectral image cube w.r.t. the spatio-spectral domain. The proposed approach allows to penalize variations in the spectral domain as well as the spatial domain to exploit the spatio-spectral correlations. Our experiments on denoising of hyperspectral images show that the proposed regularization leads to significant improvements in restoration performance over state-of-the-art methods.
Quasi-equiripple approximation of minimum phase FIR filters by updating desired response

The authors present a new numerical method for the Chebyshev approximation of minimum phase FIR digital filters. This method is based on solving a least squares (LS) problem iteratively. At each iteration, the desired response is transformed so as to have an equiripple magnitude error. This method makes it possible to design minimum phase FIR filters whose magnitude error is quasi-equiripple. Using this method, a quasi-equiripple solution is obtained very quickly. Since the proposed methods do not require any time-consuming optimisation procedure, they require less computational complexity than conventional methods. Finally, some examples to illustrate the advantage of the proposed methods are shown.
Reflectance estimation and white balancing using multiple images

We propose a novel method for reflectance estimation by using a flash/no-flash image pair. In our method, by using multiple images of the same scene taken under different lighting conditions, we estimate a reflectance component which does not depend on scene illumination, and a shading component caused by illumination lights. Moreover, we apply it to white balance correction by appropriately correcting the estimated shading components. The proposed method achieve better performance than conventional methods especially under colored illumination and mixture lighting conditions.
Progressive coding of textured 3D models

Due to the rapid development of computer and information technology, 3D modeling and rendering capabilities are becoming increasingly important in many applications, including industrial design, architecture, CAD/CAM, video games, and medical imaging. Since 3D mesh models often have the huge amount of the data, it is time-consuming to retrieve from a storage device or to download from the network. Most 3D viewing applications need to obtain the entire file of a 3D model in order to display the model, even when the user is interested only in a low-resolution version of the model. Therefore, progressive coding that enables multiresolution transmission of 3D models is desired. We propose the progressive coding scheme of 3D meshes with texture, in which we convert irregular meshes to semiregular using texture coordinates, map them on planes, and apply 2D image coding algorithm to mesh compression. As our method uses the wavelet transform, the encoded bitstream has a progressive nature. We gain high compression rate with the same visual quality as original models.
Complex approximation of 2-D digital filters with prescribed log magnitude phase response

We propose a new design algorithm for nearly linear phase 2-D IIR (FIR) digital filters with prescribed log magnitude response. The error function used is the sum of the weighted log magnitude-squared error and phase-squared error, and so it is possible to control log magnitude and phase response independently. The gradient vector of the proposed error function is easily calculated as the closed form solution because of the nature, in which the real and imaginary part of the logarithm of a complex transfer transfer function corresponds to the log magnitude and phase response, respectively. This algorithm is simple and converges quickly. Finally, we show the validity of the proposed algorithm with some examples.
HDR image compression using optimized tone mapping model

In this paper, we propose a coding algorithm for High Dynamic Range Images (HDRI). Our encoder applies a tone mapping model based on scaled μ-Lawencoding, followed by a conventional Low Dynamic Range Image (LDRI) encoder. The tone mapping model is designed to minimize the difference between the tone mappedHDRI and its LDR version. By virtue of the nature of the model, not only the quality of the HDRI but also the one of LDRI are improved, compared with a state of the art in conventional HDRI compression. Furthermore the error caused by our tone mapping model encoding is theoretically analyzed.
Fast least squares algorithm for linear phase FIR filters

In this paper, we present a new least-squares approach for linear phase FIR filters. Here, we convert a least-squares method into the problem of constructing a system of orthogonal functions. The proposed design algorithm needs only O(N) computations. Moreover the all processes of our algorithm is computed analytically, and so numerical errors can be reduced. Some examples are given to show the excellent performance of the algorithm.
Kinematics based motion compression for human figure animation

The paper presents a technique for compression of motion data using an inverse kinematics algorithm. In a motion chain, the movement of each joint is represented by a series of vector signals in 3D space. In general, specific types of joints, such as end effectors, often require higher precision than other general types of joints in, for example, CG animation and robot manipulation. When compressing these motion data, the distortion of a parent joint due to quantization in turn affects its child joint and is accumulated to the end effector. To address this problem and control the movement of the whole body, we propose a prediction method based on inverse kinematics. Our method achieves efficient compression with a high compression rate and high quality of the motion data. By comparing with a conventional prediction, we demonstrate the advantage of ours with some examples.
Vectorial total variation based on arranged structure tensor for multichannel image restoration

We propose a new regularization function, named as Arranged Structure tensor Total Variation (ASTV), for multichannel image restoration. Since the standard structure tensor is a matrix whose eigenvalues well encodes local neighborhood information of an image, there has been proposed vectorial total variation based on the structure tensor for image regularization. However, the correlation among the channels cannot be measured by the structure tensor because the discrete differences of all the channels are just summed up in the entries of the structure tensor. On the other hand, ASTV is based on a newly-defined arranged structure tensor that becomes an approximately low-rank matrix when multichannel images have strong correlation among their channels. This suggests that penalizing the nuclear norm of the arranged structure tensor is a reasonable regularization for multichannel images, leading to the definition of ASTV. Experimental results illustrate the advantage of ASTV over a state-of-the-art vectorial total variation based on the structure tensor.
High-power operation of an argon excimer laser with a MgF/sub 2/ and SiC cavity

An intense coherent light pulse whose energy, height and width (FWHM) were 80 mJ, 16 MW, and 5 ns, respectively, was obtained as 126 nm from an argon excimer laser. A superpoished SiC mirror was used in combination with a MgF/sub 2/ output coupler for an electron-beam-pumped argon excimer laser. The SiC mirror had a reflectivity at 126 nm as high as 47% and was highly damage resistant. Laser mirror materials for high-power short-wavelength lasers are discussed. Surface damage on the MgF/sub 2/ output mirror could not be avoided; this prevented stable operation of the argon excimer laser. In order to obtain such high power from the argon excimer laser, it is necessary to construct the resonator without a MgF/sub 2/ coupler. An unstable resonator is considered as the best candidate for the stable operation of the argon excimer laser.
Position-Based Keyframe Selection for Human Motion Animation

This paper proposes a method for keyframe selection of captured motion data. Motion capture systems have been widely used in movies, games and human motion analysis. Most of the previous methods make use of the rotation angles directly and measure a cost of the rotation curves for selection of keyframes. One drawback of these methods is that they do not directly control the positions of the joints in the 3D space. Our method proposes a position-based keyframe detection scheme. In our framework, frames are decimated one by one with measuring the positions of all the joints. We make use of the cost that is the sum of all the joint position differences. Then, a frame with the lowest cost is decimated after measuring those costs about frames. We demonstrate it in experimental section by several typical motions
Similarity detection of 3D meshes using 2D hierarchical regular grids

The paper presents a study on similarity detection of different 3D meshes. Our method consists of three steps. First, 3D meshes are aligned by PCA such that all of them have a same principal axis. Each rotated surface is approximated by a deformed cylinder with regular grids. Then we compare the distances by calculating their correlation. These steps are done in a hierarchical fashion. We demonstrate with examples that the proposed method can detect the similarity precisely and easily.
Improved eigenvalue shrinkage using weighted Chebyshev polynomial approximation

We propose an eigenvalue shrinkage method with a modified Chebyshev polynomial approximation (CPA). The eigenvalue shrinkage has been used in many fields of signal and image processing. However, the shrinkage takes enormous computation time especially in the case that a matrix constructed from a signal or image becomes very large, i.e., eigendecomposition can hardly be performed. The CPA is an approximation method of the shrinkage function that avoids the eigendecomposition of the matrix. Unfortunately, it is known that the CPA generates Gibbs phenomenon around points of discontinuity for approximating an ideal response. The Chebyshev-Jackson polynomial approximation (CJPA) will alleviate the problem, but the transition bandwidth becomes wide, which is an undesired characteristic for some applications. In this paper, we propose an eigenvalue shrinkage method with the reduced Gibbs phenomenon by modifying the CPA using the weighted least squares approach. Our method can reduce the error as well as the CJPA. Furthermore, it yields the narrow transition band. Some experimental results on spectral clustering validate the effectiveness of the method.
Acquisition and Encoding of High Dynamic Range Images using Inverse Tone Mapping

A two layer coding algorithm for high dynamic range images is discussed. In the first layer, a low dynamic range image is encoded by a conventional codec, and then the residual information that represents the difference between an original and the decoded images in the first layer is encoded in the second layer, which realizes compatibility with conventional image file formats. Our method utilizes the approximation of an inverse tone mapping function that reduces the high dynamic range to a displayable range. Our algorithm significantly improves a compression performance, compared to conventional methods.
Design of biorthogonal filter banks composed of linear phase IIR filters

Since IIR filters have lower computational complexity than FIR filters, some design methods for IIR filter banks have been presented in the recent literature. Smith et al. (1990) have proposed a class of linear phase IIR filter banks. However this method restricts the order of the numerator to be odd and, moreover, has some drawbacks. In this paper we present two design methods for linear phase IIR filter banks. One is based on Lagrange-Multiplier method, in which optimal IIR filter banks in least squares sense are obtained. In the other approach, IIR filter banks with the maximum number of zeros are derived analytically.
Fast regular mesh approximation of 3D models by cylindrical mapping

One most challenging issue is compactly encoding a huge 3D model data into a 2D image data. We discuss enhancement of its process speed by proposing a method for a cylindrical-mapping of 3D models which maps a model to a cylindrical image quickly. Our method is based on projection-type, not the ray tracing-type generally used, and makes it possible to encode models faster.
Image enhancement method for underwater images based on discrete cosine eigenbasis transformation

This paper proposes a novel image enhancement method for underwater images based on discrete cosine eigenbasis transformation (DCET). Since underwater images have a global strong dominant color, their colorfulness and contrast are often degraded. Typical color correction methods for natural images, i.e., computational color constancy, achromatize the colored illuminant of input images by dividing with an estimated illuminant color. However, this procedure produces unwanted color artifacts for underwater images. To solve this problem, we introduce a novel assumption that achromatic illuminant images should have the DCT basis vectors as their principal component vectors, which is termed as discrete cosine eigenbasis (DCE). According to the assumption, we achromatize underwater images by using a DCET that transforms the input image to the images having the DCE. By incorporating post image enhancement techniques, the proposed method provides sharper and brighter visual quality than conventional color correction and image enhancement methods.
A new design of type IV digital differentiators based on maximal linearity constraints applied at /spl omega/=/spl pi//2

Maximal linear FIR digital differentiators are preferred over others, like minimax designs, for narrow band applications. Most of the existing maximally linear designs achieve maximal linearity at zero frequency, and relatively lower attention has been given to the designs accurate in mid and higher frequency bands. Moreover, the existing maximally linear designs, with few exceptions, are type III filters, which are accurate in very narrow frequency bands. We present a type IV design accurate for midband frequencies, by forcing the maximal linearity constraints at half of the Nyquist frequency. The design problem is formulated as the solution of a system of linear equations, obtained by imposing maximal linearity constraints to the general frequency response of the filter. Certain special characteristics of the determinant of the coefficients matrix of these equations are explored and used in derivation of the explicit formulas for the impulse response coefficients. It is shown that the design is accurate in much wider band as compared to its type III counterpart.
Regular 3D mesh reconstruction based on cylindrical mapping

Complete 3D surface models of real objects can be obtained by integrating several range images, each representing a different view of the object. The mesh generated for these models have irregular connectivity in general. Several remeshing techniques have been proposed to approximate the irregular mesh models with regular meshes, which have several advantages in several applications. We present a prototype of a system to generate regular 3D models of real objects with a simple setup of a single 3D scanner and a turntable. Multiple scans representing different parts of the object surface are mapped on a 2D plane, which facilitates the processes of smooth integration, hole filling and regularization. The complete 3D surface model is represented like a 2D image, on which existing and new signal and image processing techniques can be applied easily.
Bilateral filtering based watermarking for high dynamic range image

As the future of digital photography, high dynamic range imaging (HDRI) has been widely applied in daily applications. However, since the visual quality of HDR images is very sensitive to luminance value variations, the conventional watermarking methods present inefficiency because of introducing many visible image distortions. In this paper, a watermarking scheme targeting HDR images is proposed, which is based on bilateral filtering. Watermarks are embedded into detail part of HDR image which is the residual image between the HDR image and the bilateral filtered image. The objective quality of the watermarked images can be significantly improved by an average of 20dB in PSNR with corresponding improvement in subjective quality. It also shows higher robustness against tone mapping operations.
A new resolution progressive coding scheme using a sorting algorithm

It has been proposed that resolution progressiveness is one of the most important techniques for image compression. In this paper, we offer a new resolution progressive coding method. The main point stresses how to exploit self-similarity across different scales of wavelet transform by sorting the coefficients. Wavelet transformed images inherently have correlation between subbands at different levels in a spatial decomposition hierarchy, and thus exploiting the correlation of the hierarchy helps to compress the amount of information of images. A number of methods with SNR progressive coding have been presented so far; however, this proposed method concerns resolution progressiveness as well as SNR progressiveness, which can be only applied in each subband, and still be effective and computationally very simple for image compression.
Local abundance regularization for hyperspectral sparse unmixing

Hyperspectral sparse unmixing is a task to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity. The abundance has a unique property, i.e., high spatial correlation in local regions. This is due to the fact that the endmembers existing in the region are highly correlated. It implies the low rankness of the abundance in the term of endmember. Coming from this prior knowledge, it is expected that considering the low-rank local abundance to the sparse unmixing problem improves the estimation performance. In this paper, we exploit the low-rank local abundance by applying the weighted nuclear norm to the abundance matrix for spatially and spectrally local regions, and add it to the conventional method. We conduct experiments assuming either pure pixels exist on the data or not. The experiment shows that our method yields competitive results and improves the conventional method.
Embedded Key-Frame Extraction for CG Animation by Frame Decimation

This paper proposes a method for key-frame selection of captured motion data. In many cases, it is desirable to obtain a compact representation of the human motion. Key-framing is often used to express CG animation with a set of frames. In general, the animation is described by a set of curves that give the value of the rotation of all joints in each frame. Our method automatically detects the key-frames in captured motion data by using frame decimation. We decimate less important frames one by one, and then rank them by their importance. Our method has an embedded property, that is, all the frames are ranked by their importance, and thus users can specify any number of keyframes from one data set. We demonstrate the validity of our method in the experimental section by several typical motions such as walking and throwing
Flexible and effective High Dynamic Range image coding

This paper presents an algorithm based on a two-layer coding scheme, where the original information is represented by means of a Low Dynamic Range (LDR) image, obtained by applying a tone mapping operator to the original HDR (High Dynamic Range), plus an enhancement layer, which allows to recover the full dynamic range. More specifically, the original HDR is represented with a format similar to the well known RGBE, which uses a shared exponent to compactly represent floating point numbers. With respect to the original RGBE, here the mantissa is composed by an approximation of the LDR image while the shared exponent represents the enhancement layer. This particular choice allows to split the original HDR into a color image, the mantissa, and a grayscale image, the exponent, which are very smooth signals that can be efficiently compressed by conventional image coding methods. With respect to already proposed similar schemas, two desirable features are then provided: a high coding efficiency, combined with the possibility to retrieve a displayable version of the original content.
Real Time 3D Avatar Transmission using Cylinder Mapping

In this paper, we propose the client-server system which accomplishes acquisition, transmission and reconstruction of the 3D shape at a remote location in real time. The server consists of stereo cameras and a PC cluster, in which each PC creates a range image using multiple color images obtained from the stereo cameras connected to it. All PCs on the server side work in synchronization and generate range images in parallel with each other, which are then transmitted to the client as a point cloud. On the client side, the 3D model is reconstructed using a received point cloud by the point-based rendering method. We propose the cylindrical mapping method, which removes overlaps and annoying artifacts efficiently before the rendering process. All the procedure of generation, transmission, and reconstruction of successive range images is done in real time.
Weight optimization for multiple image integration

We propose a denoising technique using multiple image integration. When acquiring a dark scene, the detail of the dark area is often deteriorated by sensor noise. A simple image integration inherently has the capability of reducing random noises. In this paper we develop the denoising performance of the multiple image integration by optimizing weight maps. We determine the optimal weight by solving a convex optimization problem. Through some experimental results, we show the weight optimization significantly improves the de-noising performance.
Color transform between image pair using covariance correspondences of local color distributions

In the guided filter that performs operations such as denoising and contrast correction with the help of a guide image, the positions of corresponding subjects need to be completely aligned, otherwise the misaligned regions in the output image are deteriorated by blur. In this paper, we propose a guided filter for images which include moving dynamic regions. Our filter uses correspondences of local covariance matrices instead of using the conventional pixel-to-pixel correspondences. In addition, we also propose a classification method to detect the dynamic regions by using the support vector machine. Combining two kinds of guided filters for static/dynamic regions, more natural resulting images are obtained.
Disparity and occlusion estimation for multiview image sequences using dynamic programming

An efficient disparity estimation and occlusion detection and characterization algorithm for multiocular systems is presented. A dynamic programming algorithm, using a multiview matching cost as well as pure geometrical constraints, is used to provide an estimate of the disparity field and to identify occluded areas. An important advantage of this approach is that not only are the occluded points simultaneously detected, but they are also characterized by the number of views where each point is occluded. Specifically, a "state" map describing the number of matches for each imaged pixel and thus identifying occluded points in the multiview sequence is produced. The disparity and state information is then applied to obtain virtual images from intermediate viewpoints. Experimental results, obtained using a four-view image sequence, illustrate the performance of the proposed technique.