nils-herrmann/clean_scirepeval huggingface dataset:
Multi-Objective Optimization and Characterization of Pareto Points for Scalable Coding

In this paper, we formulated the optimal bit-allocation problem for a scalable codec for images/videos as a graph-based constrained vector-valued optimization problem with many optimal solutions, which are referred to as Pareto points. Pareto points are generally derived using weighted sum scalarization; however, it has yet to be determined whether all Pareto points can be derived using this approach. This paper addresses this issue. When presented as a theorem, our results indicate that as long as the rate-distortion function of each resolution is strictly decreasing and convex and the Pareto points form a continuous curve, then all Pareto points can be derived using scalarization. The theorem is verified using the state-of-the-art scalable coding method H.264/SVC and a scalability extension of High Efficiency Video Coding (HEVC). We highlight a number of easily interpretable Pareto points that represent a good trade-off between candidate resolutions. The proximity point is defined as the Pareto point closest to the ideal performance for each resolution. We also model the Pareto points as a function of total bit rate and demonstrate that the Pareto points at other target bit rates can be predicted.
Interlayer Bit Allocation for Scalable Video Coding

In this paper, we present a theoretical analysis of the distortion in multilayer coding structures. Specifically, we analyze the prediction structure used to achieve temporal, spatial, and quality scalability of scalable video coding (SVC) and show that the average peak signal-to-noise ratio (PSNR) of SVC is a weighted combination of the bit rates assigned to all the streams. Our analysis utilizes the end user's preference for certain resolutions. We also propose a rate-distortion (R-D) optimization algorithm and compare its performance with that of a state-of-the-art scalable bit allocation algorithm. The reported experiment results demonstrate that the R-D algorithm significantly outperforms the compared approach in terms of the average PSNR.
A backward wavelet remesher for level of detail control and scalable coding

Multi-resolution and wavelet analysis have generated considerable interest in the field of mesh surface representation. In this paper, we propose a backward, coarse-to-fine framework that derives a semi-regular approximation of an original mesh, and demonstrate its effectiveness on level-of-detail and scalable coding applications. The framework is flexible and simple because the position of a new vertex at a finer resolution can be derived in a closed form, based on the affine combination of a subdivision scheme, the original mesh, and “new” information about the wavelet coefficients. We report the results of experiments on both applications; and also compare the scalable coding results with those of other methods.
Mixture of Gaussian Blur Kernel Representation for Blind Image Restoration

Blind image restoration is a nonconvex problem involving the restoration of images using unknown blur kernels. The success of the restoration process depends on three factors: first, the amount of prior information concerning the image and blur kernel; second, the algorithm used to perform restoration; and third, the initial guesses made by the algorithm. Prior information of an image can often be used to restore the sharpness of edges. In contrast, there is no consensus concerning the use of prior information in the restoration of images from blur kernels, due to the complex nature of image blurring processes. In this paper, we model a blur kernel as a linear combination of basic two-dimensional (2-D) patterns. To illustrate this process, we constructed a dictionary comprising atoms of Gaussian functions derived from the Kronecker product of 1-D Gaussian sequences. Our results show that the proposed method is more robust than other state-of-the-art methods in a noisy environment, due to its increased signal-to-noise ratio (ISNR). This approach also proved more stable than the other methods, due to the steady increase in ISNR as the number of iterations is increased.
Wavelet Bayesian Network Image Denoising

From the perspective of the Bayesian approach, the denoising problem is essentially a prior probability modeling and estimation task. In this paper, we propose an approach that exploits a hidden Bayesian network, constructed from wavelet coefficients, to model the prior probability of the original image. Then, we use the belief propagation (BP) algorithm, which estimates a coefficient based on all the coefficients of an image, as the maximum-a-posterior (MAP) estimator to derive the denoised wavelet coefficients. We show that if the network is a spanning tree, the standard BP algorithm can perform MAP estimation efficiently. Our experiment results demonstrate that, in terms of the peak-signal-to-noise-ratio and perceptual quality, the proposed approach outperforms state-of-the-art algorithms on several images, particularly in the textured regions, with various amounts of white Gaussian noise.
Segmenting Microarray Image Spots using an Active Contour Approach

Inspired by Paragious and Deriche's work, which unifies boundary-based and region-based image partition approaches, we integrate the active contour(snake) model and the Fisher criterion to capture, respectively, the boundary and region information of microarray images. We then use the proposed algorithm to automatically segment the spots in the microarray images, and compare our results with those obtained by commercial software.
Parameter estimation and denoising of 2-D noisy fractional Brownian motion using non-orthogonal wavelets

Fractional Brownian motion (fBm) is a non-stationary stochastic model, which has a 1/f spectrum and statistical self-similar property. We extend the proposed methods of Hwang to an isotropic 2-D noisy fBm image. The extension is not straightforward; although one can obtain the fractal parameter of an isotropic fBm by averaging of the estimated fractal parameters from several directions by means of the 1-D fractal parameter estimation algorithm, this approach does not perform well in practice. It was shown by Hwang that it requires more than 1000 sampled points for a robust 1-D fractal parameter estimation. For a median size image (say with size 256 by 256 or smaller), there is not enough pixels at each direction for a robust 1-D fractal parameter estimation. Thus, alternative methods must be developed in order that the robustness fractal estimation from a noisy fBm image with small size can be achieved. In this paper, we show that the wavelet transform of an isotropic fBm image at each scale is a two-dimensional weakly stationary process at both the horizontal and vertical directions. Thus, robust fractal parameter estimation can be obtained from two-dimensional wavelet coefficients, even for a small noisy fBm image. We propose a fractal parameter estimation algorithm which formulates the robust fractal parameter estimation problem as the characterization of a composite singularity from the autocorrelation of wavelet transforms of a noisy fBm image.
Segmentation of 3D textured images using continuous wavelet transform

A common assumption of the shape from texture problem is that a perceived image mainly contains only one type of texture with the same surface orientation. Unfortunately, a natural image is often composed of more than one textures. In order to solve the shape from texture problem in a practical manner, we need to segment 3D textured images. In this paper, we propose a new algorithm for the task. We estimate the local surface orientations from the the scales of the ridge points of continuous wavelet transform. Then, the local surface orientations are used as the features for texture segmentation. Textured images synthesized from Brodatz's album and several natural images demonstrate the performance of our method.
Wavelet analysis for brain-function imaging

The authors present a new algorithmic procedure for the analysis of brain images. This procedure is specifically designed to image the activity and functional organization of the brain. The authors' results are tested on data collected and previously analyzed with the technique known as in vivo optical imaging of intrinsic signals. The authors' procedure enhances the applicability of this technique and facilitates the extension of the underlying ideas to other imaging problems (e.g., functional MRI). The authors' thrust is two fold. First, they give a systematic method to control the blood vessel artifacts which typically reduce the dynamic range of the image. They propose a mathematical model for the vibrations in time of the veins and arteries and they design a new method for cleaning the images of the vessels with the highest time variations. This procedure is based on the analysis of the singularities of the images. The use of wavelet transform is of crucial importance in characterizing the singularities and reconstructing appropriate versions of the original images. The second important component of the authors' work is the analysis of the time evolution of the fine structure of the images. They show that, once the images have been cleaned of the blood vessel vibrations/variations, the principal component of the time evolutions of the signals is due to the functional activity following the stimuli. The part of the brain where this function takes place can be localized and delineated with precision.
Enhancing image watermarking methods with/without reference images by optimization on second-order statistics

The watermarking method has emerged as an important tool for content tracing, authentication, and data hiding in multimedia applications. We propose a watermarking strategy in which the watermark of a host is selected from the robust features of the estimated forged images of the host. The forged images are obtained from Monte Carlo simulations of potential pirate attacks on the host image. The solution of applying an optimization technique to the second-order statistics of the features of the forged images gives two orthogonal spaces. One of them characterizes most of the variations in the modifications of the host. Our watermark is embedded in the other space that most potential pirate attacks do not touch. Thus, the embedded watermark is robust. Our watermarking method uses the same framework for watermark detection with a reference and blind detection. We demonstrate the performance of our method under various levels of attacks.
Singularities and noise discrimination with wavelets

One can detect and characterize the singularities of a signal from the evolution of the wavelet transform coefficients across scales. The authors discriminate signal information from noise by using some prior knowledge of the properties of singularities. The wavelet transform of the signal is processed in order to remove the singularities created by the noise. The authors restore a sharp signal where part of the noise has been suppressed. Examples in one and two dimensions are shown.<<ETX>>
Variational calculus approach to multiresolution image mosaic

Image mosaic combines two or more images. It has found many applications in computer vision, image processing, and computer graphics. A common goal of the problem is to join two or more images such that there is an invisible boundary around the seam line and the mosaic image is as little distorted from the original images as possible. We propose a new image mosaic method by wavelet multiresolution analysis and variational calculus. We first project the images into wavelet spaces. The projected images at each wavelet space are then blended. Variational calculus techniques are applied to balance the quality between the smoothness around the seam line and the fidelity of the combined image relative to the original images in image blending. A mosaic image is finally obtained by summing the blended images at the wavelet spaces. Experimental results based on our method are demonstrated.
EMD Revisited: A New Understanding of the Envelope and Resolving the Mode-Mixing Problem in AM-FM Signals

Empirical mode decomposition (EMD) is an adaptive and data-driven approach for analyzing multicomponent nonlinear and nonstationary signals. The stop criterion, envelope technique, and mode-mixing problem are the most important topics that need to be addressed in order to improve the EMD algorithm. In this paper, we study the envelope technique and the mode-mixing problem caused by separating multicomponent AM-FM signals with the EMD algorithm. We present a new necessary condition on the envelope that questions the current assumption that the envelope passes through the extreme points of an intrinsic mode function (IMF). Then, we present a solution to the mode-mixing problem that occurs when multicomponent AM-FM signals are separated. We experiment on several signals, including simulated signals and real-life signals, to demonstrate the efficacy of the proposed method in resolving the mode-mixing problem.
Constrained Null Space Component Analysis for Semiblind Source Separation Problem

The blind source separation (BSS) problem extracts unknown sources from observations of their unknown mixtures. A current trend in BSS is the semiblind approach, which incorporates prior information on sources or how the sources are mixed. The constrained independent component analysis (ICA) approach has been studied to impose constraints on the famous ICA framework. We introduced an alternative approach based on the null space component (NCA) framework and referred to the approach as the c-NCA approach. We also presented the c-NCA algorithm that uses signal-dependent semidefinite operators, which is a bilinear mapping, as signatures for operator design in the c-NCA approach. Theoretically, we showed that the source estimation of the c-NCA algorithm converges with a convergence rate dependent on the decay of the sequence, obtained by applying the estimated operators on corresponding sources. The c-NCA can be formulated as a deterministic constrained optimization method, and thus, it can take advantage of solvers developed in optimization society for solving the BSS problem. As examples, we demonstrated electroencephalogram interference rejection problems can be solved by the c-NCA with proximal splitting algorithms by incorporating a sparsity-enforcing separation model and considering the case when reference signals are available.
Shape from texture based on the ridge of continuous wavelet transform

We propose a new shape from texture method based on the ridge of continuous wavelet transform. This method determines the orientations of a planar surface in a direct way under the perspective projection model. The variations of the image projected from a planar surface can be accurately characterized by the ridge of the continuous wavelet transform. The ridge of the 1-D signal and 2-D image are represented as a ridge curve and ridge plane, respectively. Ridges represent the energy concentration in the time-frequency plane where the energy is a local maxima. We show that the ridge of the projected image is a parabolic plane with a rotation angle equal to the tilt angle of the planar surface. The ridge is then rotated with the angle such that the slant effect appears in the X-axis and plays no role along the Y-axis. As a result, the rotated ridge plane can be regarded as the plane composed of many 1-D ridge curves. The slant angle of the 2-D image is thus obtained from the derived slant angle of the 1-D signal. A voting method and a curve fitting method are developed to obtain the slant angle of the 1-D signal. Several synthetic and real-world images have demonstrated the robustness and accuracy of our method.
Enhancing image watermarking methods by second order statistics

A pirate attack on an image aims to create an invisible modification of it. We propose a watermarking strategy which applies optimization techniques to the second order statistics of perceptually unaltered modifications of an image. The solution gives two orthogonal spaces. One of them characterizes most of the variations in the modification of the image. Our watermark is embedded in the other space that most potential pirate attacks do not touch. Thus, the embedded watermark is robust. We also show that our method is able to enhance many existing watermarking strategies. We also demonstrate the performance of our method.
Estimation of 2-D noisy fractional Brownian motion and its applications using wavelets

The two-dimensional (2-D) fractional Brownian motion (fBm) model is useful in describing natural scenes and textures. Most fractal estimation algorithms for 2-D isotropic fBm images are simple extensions of the one-dimensional (1-D) fBm estimation method. This method does not perform well when the image size is small (say, 32x32). We propose a new algorithm that estimates the fractal parameter from the decay of the variance of the wavelet coefficients across scales. Our method places no restriction on the wavelets. Also, it provides a robust parameter estimation for small noisy fractal images. For image denoising, a Wiener filter is constructed by our algorithm using the estimated parameters and is then applied to the noisy wavelet coefficients at each scale. We show that the averaged power spectrum of the denoised image is isotropic and is a nearly 1/f process. The performance of our algorithm is shown by numerical simulation for both the fractal parameter and the image estimation. Applications to coastline detection and texture segmentation in a noisy environment are also demonstrated.
An Automatic Eye Wink Interpretation System for the Disable

This paper proposes an automatic eye wink interpretation system for the severely handicapped people. First, we apply the support vector machine (SVM) and template matching algorithm to detect the eyes and then track the eye winks. Then, we convert the sequence of eye winks into code (binary digit) sequence. Finally, we use the dynamic programming to translate the eye wink sequence to a certain command for human-machine interface. In the experiments, our system demonstrates very good performance and high accuracy.
Wavelet based active contour model for object tracking

We propose an integrated wavelet-based framework for an active contour model (snake) which is used for the motion tracking of deformable objects in a sequence of images. The input image frame is decomposed into a multiresolution representation using a wavelet transform. First, the wavelet transform coefficients are used in a multiresolution motion estimation process to find the initial contour in the frame. The presented multiresolution motion estimation method allows larger movement of the tracked object than the traditional image-based motion estimation. Secondly, the wavelet transform modulus at each scale is considered in the energy function of the active contour model. The application of biological cell tracking using the proposed method is shown as an example.
A proximal method for the K-SVD dictionary learning

In this paper, we propose a dictionary updating method and show numerically that it can converge to a dictionary that outperforms the dictionary derived by the K-SVD method. The proposed method is based on the proximal point approach used in the convex optimization algorithm. We incorporate the approach into the well-known MOD and combine the result with the K-SVD method to obtain the proposed method. We analyze the complexity of the proposed method and compare it with that of the K-SVD method. The results of experiments demonstrate that our method outperforms K-SVD with only a slight increase in the execution time.
Timing acquisition for fractal modulation in Gaussian white and 1/f channels

We propose a clock acquisition algorithm for timing recovery for fractal modulation in additive white Gaussian noise (AWGN) and 1/f channels. Our acquisition algorithm uses exclusively the data redundancy inherently built in the fractal modulation to locate the beginning of timing of all the subbands simultaneously. Different diversity techniques can then be applied after the beginning of the data block is obtained. Our acquisition functions are derived by using the maximum-likelihood method. Using a serial search algorithm to maximize the acquisition function, we may find the timing point. Simulations have been conducted in evaluating the mean acquisition time of our algorithm.
Extending depth of field in noisy light field photography

A robust depth of field (DOF) extension algorithm was proposed based on the refocusing property of a light field photograph and the depth-from-defocus approach of multi-focus image fusing. The main techniques of the algorithm are depth estimation and all-in-focus image estimation. By making use of the redundancy of a light field photograph, we leverage both estimations in a noisy environment. For the noise level smaller than 25dB, the proposed algorithm is still robust and the performance is better than other methods both in PSNR and SSIM. We conclude that the algorithm is robust to high noise.
Mode Selection and Optimal Rate Control for Video Coding using an and-or Tree Representation

We propose an AND-OR tree-based approach for structuring a variety of mode selections in a hybrid video coding system. The proposed approach can systematically analyze the input sequence and transform it into an AND-OR tree representation to allocate bits to each node of the tree. The motion vector of our system is estimated in the wavelet domain, where wavelet coefficients in different scales can be obtained by using different wavelets. We demonstrate the performance of the new features, and compare our codec's performance with that of an H.264/AVC-like implementation.
Design Error-Resilient Multiple Substreams 3D Coder Including Receiver Post-Processing in Analysis

We propose an error control scheme for video communications over lossy channels. The proposed algorithm uses receiver post-processing in analysis to coordinate with multiple encoded streams which is capable of handling error concealment and error protection to achieve robust transmissions. Unlike previous methods, our algorithm focuses on joint optimizing multiple substreams distortion with concealment over error prone channels. The algorithm minimizes the expected rate-distortion function to achieve the optimal FEC result. In the experiments, we demonstrate the effectiveness of our method by using a 3-D SPIHT algorithm. Simulation results show that the proposed protection strategy achieves about 2 dB higher peak signal-to-noise ratio compared to conventional method.
Deriving 3D shape properties by using backward wavelet remesher

It is important to determine 3D shape properties of a population of 3D mesh models in biomedical imaging issues. In contrast to conventional 3D shape analysis techniques focusing on applications like shape matching and shape retrieval, we propose in this paper a strategy capable to collect statistical information of multiple triangular mesh models. Our method operates in a coarse-to-fine fashion based on wavelet synthesis. Hence, its analysis result can be invariant against the triangular tiling of the input mesh model. This characteristic enables us to make a comparison among multiple mesh models simultaneously. The experiment results show that our method can extract 3D shape components of each observation scale and is efficient in estimating an average shape and visualizing 3D shape variability of multiple triangular mesh models.