nils-herrmann/clean_scirepeval huggingface dataset:
Robust Web Image/Video Super-Resolution

This paper proposes a robust single-image super-resolution method for enlarging low quality web image/video degraded by downsampling and compression. To simultaneously improve the resolution and perceptual quality of such web image/video, we bring forward a practical solution which combines adaptive regularization and learning-based super-resolution. The contribution of this work is twofold. First, we propose to analyze the image energy change characteristics during the iterative regularization process, i.e., the energy change ratio between primitive (e.g., edges, ridges and corners) and nonprimitive fields. Based on the revealed convergence property of the energy change ratio, appropriate regularization strength can then be determined to well balance compression artifacts removal and primitive components preservation. Second, we verify that this adaptive regularization can steadily and greatly improve the pair matching accuracy in learning-based super-resolution. Consequently, their combination effectively eliminates the quantization noise and meanwhile faithfully compensates the missing high-frequency details, yielding robust super-resolution performance in the compression scenario. Experimental results demonstrate that our solution produces visually pleasing enlargements for various web images/videos.
A novel survival of the fittest genetic algorithm

Considering the relationship between the variety of evolution population and evolution times, a novel closed crossing avoidance strategy is put forth in this paper. Based on it, a novel survival of the fittest genetic algorithm is present. The algorithm can avoid close breeding effectively and the thought of survival of the fittest is externalized. It has been proved that the algorithm can converge to an optimal solution globally. Simulation shows that the algorithm present in this paper is an efficient contrast with the simple genetic algorithm.
A Fast Downsizing Video Transcoder for H.264/AVC with Rate-Distortion Optimal Mode Decision

This paper focuses on the mode decision and motion selection problem when H.264/AVC video streams are transcoded in spatial resolution. A fast downsizing transcoding scheme is developed in which a new rate-distortion (R-D) optimal mode decision mechanism is presented for high speed transcoding as well as high coding efficiency. A model for estimating relative prediction errors is applied in this paper, which is free from computation of interpolation and SAD/SSD computation. Based on the selected model, a motion refinement within a distance of 1 pixel is performed after mode decision. Experimental results demonstrate that our method can significantly speed up the spatial resolution reduction process, while achieving high coding efficiency
Personalized Search Inspired Fast Interactive Estimation of Distribution Algorithm and Its Application

Interactive evolutionary algorithms have been applied to personalized search, in which less user fatigue and efficient search are pursued. Motivated by this, we present a fast interactive estimation of distribution algorithm (IEDA) by using the domain knowledge of personalized search. We first induce a Bayesian model to describe the distribution of the new user’s preference on the variables from the social knowledge of personalized search. Then we employ the model to enhance the performance of IEDA in two aspects, that is: 1) dramatically reducing the initial huge space to a preferred subspace and 2) generating the individuals of estimation of distribution algorithm(EDA) by using it as a probabilistic model. The Bayesian model is updated along with the implementation of the EDA. To effectively evaluate individuals, we further present a method to quantitatively express the preference of the user based on the human-computer interactions and train a radial basis function neural network as the fitness surrogate. The proposed algorithm is applied to a laptop search, and its superiorities in alleviating user fatigue and speeding up the search procedure are empirically demonstrated.
Fingerprint image enhancement using a fast Gabor filter

Automatic fingerprint identification is one of the most important biometric technologies. Automatic fingerprint identification depends mainly on the comparison of ridge characteristics. The ridge characteristics include ridge ending, ridge bifurcation. How to extract those minutiae effectively is the key of automatic fingerprint identification. This paper uses a square Gabor filter using local average frequency. The method can overcome the blocky effect of Lin Hong's Gabor filter . This paper proposes a new evaluation GI(Goodness Index) method of the fingerprint image enhancement algorithm. This evaluation GI method has a wide prospect.
Optimal Bit Allocation for CTU Level Rate Control in HEVC

For High Efficiency Video Coding (HEVC), the R–<inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> scheme is the latest rate control (RC) scheme, which investigates the relationships among allocated bits, the slope of rate-distortion (R-D) curve <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>, and quantization parameter. However, we argue that bit allocation in the existing R–<inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> scheme is not optimal. In this paper, we therefore propose an optimal bit allocation (OBA) scheme for coding tree unit level RC in HEVC. Specifically, to achieve the OBA, we first develop an optimization formulation with a novel R-D estimation, instead of the existing R–<inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> estimation. Unfortunately, it is intractable to obtain a closed-form solution to the optimization formulation. We thus propose a recursive Taylor expansion (RTE) method to iteratively solve the formulation. As a result, an approximate closed-form solution can be obtained, thus achieving OBA and bit reallocation. Both theoretical and numerical analyses show the fast convergence speed and little computational time of the proposed RTE method. Therefore, our OBA scheme can be achieved at little encoding complexity cost. Finally, the experimental results validate the effectiveness of our scheme in three aspects: R-D performance, RC accuracy, and robustness over dynamic scene changes.
Efficient and universal scalable video coding

This paper proposes a unified efficient and universal scalable video coding framework that supports different scalabilities, such as fine granularity quality, temporal, spatial and complexity scalabilities. The proposed framework is established upon the recent studies in fine granularity scalable (FGS) video coding. It contains two key points. Firstly, in order to improve the coding efficiency of the proposed framework, more than one motion compensation loop is used. Since high quality references are introduced into the enhancement layer coding, the proposed framework can efficiently compress different-resolution video at different layers for the purpose of the complexity and spatial scalability. Secondly, the drifting reduction techniques are studied in this paper. This helps the proposed framework to maintain good performance at lower enhancement bit rates. By defining coding modes, a macroblock level control mechanism is developed to achieve a better trade-off between low drifting errors and high coding efficiency.
Deblurring Saturated Night Image With Function-Form Kernel

Deblurring saturated night images are a challenging problem because such images have low contrast combined with heavy noise and saturated regions. Unlike the deblurring schemes that discard saturated regions when estimating blur kernels, this paper proposes a novel scheme to deduce blur kernels from saturated regions via a novel kernel representation and advanced algorithms. Our key technical contribution is the proposed function-form representation of blur kernels, which regularizes existing matrix-form kernels using three functional components: 1) trajectory; 2) intensity; and 3) expansion. From automatically detected saturated regions, their skeleton, brightness, and width are fitted into the corresponding three functional components of blur kernels. Such regularization significantly improves the quality of kernels deduced from saturated regions. Second, we propose an energy minimizing algorithm to select and assign the deduced function-form kernels to partitioned image regions as the initialization for non-uniform deblurring. Finally, we convert the assigned function-form kernels into matrix form for more detailed estimation in a multi-scale deconvolution. Experimental results show that our scheme outperforms existing schemes on challenging real examples.
Total Least-Squares Solution of Active Target Localization Using TDOA and FDOA Measurements in WSN

Active target localization is one of the study emphases in wireless sensor network (WSN). When time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements are used to target localization, the measurements are nonlinearly related to the target location parameters. By the analysis of TDOA and FDOA linearized equations, when there exists sensor location error, active target localization using TDOA and FDOA measurements is a typical total least-squares (TLS) problem. The TLS solution for active target localization is presented in this paper. Its minimum square error ( MSE) is compared with Cramer-Rao lower bound (CRLB) through the simulation results.
Image Compression by Visual Pattern Vector Quantization (VPVQ)

This paper proposes a new image compression scheme by introducing visual patterns to nonlinear interpolative vector quantization (IVQ). Input images are first distorted by a generic down-sampling so that some details are removed before compression. Then, the distorted images are compressed lossly by traditional image coding scheme and transmitted to the decoder. In the decoder side, VQ indices are extracted from the decoded images to reproduce the removed details from a pre-trained codebook. One of main contributions in this paper is, we introduce visual patterns on designing the codebook, where only removed details that contain visual patterns and their original counterparts as pairs are trained. Experimental results show: (1) visual pattern blocks are easy to form clusters than original blocks; (2) the proposed scheme achieves much better performance over JPEG in terms of visual quality and PSNR.
Super-resolution for low quality thumbnail images

This paper proposes a single-image super-resolution scheme for enlarging low quality thumbnail images widely distributed on the Web, which are often generated by downsampling plus compression. To obtain visually pleasurable high-resolution versions for this kind of low-resolution images, we first adopt a PDE-based image regularization technique to alleviate the compression noise in the distorted thumbnails, and then use learning-based pair matching to further enhance the high-frequency details in the upsampled images. Experimental results show that our solution achieves better visual quality for both offline and online test images, compared with traditional methods.
Contrast Enhancement Based on Intrinsic Image Decomposition.

In this paper, we propose to introduce intrinsic image decomposition priors into decomposition models for contrast enhancement. Since image decomposition is a highly illposed problem, we introduce constraints on both reflectance and illumination layers to yield a highly reliable solution. We regularize the reflectance layer to be piecewise constant by introducing a weighted ℓ1 norm constraint on neighboring pixels according to the color similarity, so that the decomposed reflectance would not be affected much by the illumination information. The illumination layer is regularized by a piecewise smoothness constraint. The proposed model is effectively solved by the Split Bregman algorithm. Then, by adjusting the illumination layer, we obtain the enhancement result. To avoid potential color artifacts introduced by illumination adjusting and reduce computing complexity, the proposed decomposition model is performed on the value channel in HSV space. Experiment results demonstrate that the proposed method performs well for a wide variety of images, and achieves better or comparable subjective and objective quality compared with the state-of-the-art methods.
Web cartoon video hallucination

This paper addresses the super-resolution problem for low quality cartoon videos widely distributed on the web, which are generated by downsampling and compression from the sources. To effectively eliminate the compression artifacts and meanwhile preserve the visually salient primitive components (e.g., edges, ridges and corners), we propose an adaptive regularization method depending on the degradation grade of each frame, followed by learning-based pair matching to further enhance the primitives in the upsampled frames. In addition, temporal consistency is considered a directive constraint in both the regularization and enhancement processes. Experimental results demonstrate our solution achieves a good balance between artifacts removal and primitive enhancement, providing perceptually high quality super-resolution results for various web cartoon videos.
Human action recognition based on projection and mass center movement features

This paper proposes a simple and effective human action recognition algorithm based on projection and mass center movement features. Firstly, divide the original video into equal length subsequences with overlapping time window, and make moving parts detection using adjacent frame difference. Then, horizontal projection and vertical projection of binary image are made, and in order to get ride of redundant information and reduce computational burden, the principal components are extracted by PCA from a series of projection features in equal length subsequences with overlapping time window. Finally these projection features are combined with mass center movement features to identify the human action, and KNN is adopted to classify. Online video library is used in final experiments, and the experimental results show that this strategy can achieve good performance in terms of recognition rate, computational cost.
Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model

Low-light image enhancement methods based on classic Retinex model attempt to manipulate the estimated illumination and to project it back to the corresponding reflectance. However, the model does not consider the noise, which inevitably exists in images captured in low-light conditions. In this paper, we propose the robust Retinex model, which additionally considers a noise map compared with the conventional Retinex model, to improve the performance of enhancing low-light images accompanied by intensive noise. Based on the robust Retinex model, we present an optimization function that includes novel regularization terms for the illumination and reflectance. Specifically, we use  $\ell _{1}$  norm to constrain the piece-wise smoothness of the illumination, adopt a fidelity term for gradients of the reflectance to reveal the structure details in low-light images, and make the first attempt to estimate a noise map out of the robust Retinex model. To effectively solve the optimization problem, we provide an augmented Lagrange multiplier based alternating direction minimization algorithm without logarithmic transformation. Experimental results demonstrate the effectiveness of the proposed method in low-light image enhancement. In addition, the proposed method can be generalized to handle a series of similar problems, such as the image enhancement for underwater or remote sensing and in hazy or dusty conditions.
Incremental SfM based lossless compression of JPEG coded photo album

The key problem in photo album compression is how to exploit the correlation among the images. In this paper, we propose a novel incremental structure from motion (SfM) based prediction method for lossless photo album compression. Unlike the previous methods, we exploit the redundancy among images through their inherent geometric relationship generated by SfM. Based on the point cloud and camera poses, each prediction image is generated by projecting, triangulation and warping. Finally, the target image is compressed by an HEVC-like encoder with the prediction image as main reference. Experimental results demonstrate the advantage of our method, especially for images with scenes containing complicated geometric structures.
Investigation of critical dimension (CD) variation control on unstable substrate

With the development of the semiconductor devices, advanced technology node requires both tight critical dimension (CD) control and enough photo process window. In many circumstances, the unstable substrate can have great influence on photo CD, causing large variation in the performance of device. Although there have been intensive studies on this topic, we attempt to discuss the physical mechanism underneath within this paper, and to illustrate the phenomenon observed in the function layers. This paper is to investigate the contributions of several different factors on photo CD variation and discuss the possible controlling methods. Different substrate deposition methods and various surface treatment methods are applied in our experiment and the corresponding influence on CD variations are investigated. Results indicate that the surface treatments play important roles in controlling CD variation. The underlying mechanism is discussed.
Image Denoising by Exploring External and Internal Correlations

Single image denoising suffers from limited data collection within a noisy image. In this paper, we propose a novel image denoising scheme, which explores both internal and external correlations with the help of web images. For each noisy patch, we build internal and external data cubes by finding similar patches from the noisy and web images, respectively. We then propose reducing noise by a two-stage strategy using different filtering approaches. In the first stage, since the noisy patch may lead to inaccurate patch selection, we propose a graph based optimization method to improve patch matching accuracy in external denoising. The internal denoising is frequency truncation on internal cubes. By combining the internal and external denoising patches, we obtain a preliminary denoising result. In the second stage, we propose reducing noise by filtering of external and internal cubes, respectively, on transform domain. In this stage, the preliminary denoising result not only enhances the patch matching accuracy but also provides reliable estimates of filtering parameters. The final denoising image is obtained by fusing the external and internal filtering results. Experimental results show that our method constantly outperforms state-of-the-art denoising schemes in both subjective and objective quality measurements, e.g., it achieves >2 dB gain compared with BM3D at a wide range of noise levels.
Photo Stylistic Brush: Robust Style Transfer via Superpixel-Based Bipartite Graph

With the rapid development of social network and multimedia technology, customized image and video stylization have been widely used for various social-media applications. In this paper, we explore the problem of exemplar-based photo style transfer, which provides a flexible and convenient way to invoke fantastic visual impression. Rather than investigating some fixed artistic patterns to represent certain styles as was done in some previous works, our work emphasizes styles related to a series of visual effects in the photograph (e.g., color, tone, and contrast). We propose a photo stylistic brush, an automatic robust style transfer approach based on Super pixel-based BIpartite Graph (SuperBIG). A two-step bipartite graph algorithm with different granularity levels is employed to aggregate pixels into superpixels and find their correspondences. In the first step, with the extracted hierarchical features, a bipartite graph is constructed to describe the content similarity for pixel partition to produce superpixels. In the second step, superpixels in the input/reference image are rematched to form a new superpixel-based bipartite graph, and superpixel-level correspondences are generated by bipartite matching. Finally, the refined correspondence guides SuperBIG to perform the transformation in a decorrelated color space. Extensive experimental results demonstrate the effectiveness and robustness of the proposed method for transferring various styles of exemplar images, even for some challenging cases, such as night images.
Short-term PV generation forecasting based on a multi-object AVC strategy assessment system based on modified radar chart method

The AVC system is a critical automatic control system which makes contribution to securing the voltage and reactive power operation of the power network. During the closed-loop detection of the AVC system which is to be applied in sub-transmission network, much problems have been found about AVC strategies, including their failing in optimizing voltage level, power factor of 220kV transformer and active power loss, while in some occasion the switching constraints is not satisfied. The existed AVC assessment index is usually designed based on power system operation assessment index, which is not so suitable for AVC assessment. Aiming at this problem, this essay present a three-layer AVC strategy assessment index. The first two layer of the index gives assessment on the satisfaction and the effect of AVC strategy, while the third layer makes analysis about the regulation potential of the network. The fuzzy membership function is used in the designing of the first layer of index to depict the relationship between index value and satisfaction degree. Finally, a modified radar chart method is employed to illustrate the assessment result, while two evaluation functions are presented to compare different strategies. The example shows that this method is credible.
Video Coding with Spatio-Temporal Texture Synthesis

This paper presents a video coding scheme in which some texture regions are selectively removed at the encoder and recovered by synthesis at the decoder. We present region removal utilizing conventional block-based motion information rather than global motion field. Removed regions including their motion information are not coded at the encoder. We propose spatio-temporal patch-searching in texture synthesis at the decoder to recover the removed regions. Our approach is not content based and is flexible and generic to be implemented. The scheme has been integrated into H.264/AVC and achieves up to 38.8% bitrate saving at similar visual quality levels compared with H.264/AVC.