nils-herrmann/clean_scirepeval huggingface dataset:
Stereo matching using hierarchical belief propagation along ambiguity gradient

This paper proposes a stereo matching algorithm based on hierarchical belief propagation and occlusion handling. We define a new order for message passing in belief propagation instead of the scanline approach. The primary assumption is that a pixel with a well-defined minimum in its likelihood field is more likely to contain a correct disparity, when compared to a pixel having an ill-defined minimum with several local minima. The order for message passing is determined by the variance of likelihood field at each pixel. The variances evaluate the ambiguity of likelihood fields, and the messages are hierarchically updated along the gradient of ambiguity. The experimental results show that the proposed method estimates the disparities correctly in the hard regions such as large occlusions and textureless regions. The proposed algorithm is currently tied with the best performing algorithm on the Middlebury stereo site.
Lossless Compression of Color Filter Array Images by Hierarchical Prediction and Context Modeling

This paper presents an encoder for the lossless compression of color filter array (CFA) data, which consists of a hierarchical predictor and context-adaptive arithmetic encoder. In hierarchical prediction, the subsampled images are encoded in order; each of the subimages contains only one color component (red, green, or blue) in the case of a Bayer CFA image. By subsampling, the green pixels are separated into two sets, one of which is encoded by a conventional grayscale encoder, and then is used to predict the green pixels in the other set. Both the sets of greens are then used to predict the reds, and the green and red pixels are used to predict the blues. Throughout this process, the predictors are designed considering the direction of the edges in the neighborhood. By gathering some information from the prediction process, such as edge activity and neighboring errors, the magnitude of prediction error is also estimated. From this, the probability distribution function of prediction error conditioned on neighboring pixels, i.e., the context is estimated, and context-adaptive arithmetic encoding is applied to reduce the resulting bits further. The experimental results on real and simulated CFA images show that the proposed method produces less bits per pixel than the conventional lossless image compression methods and recently developed lossless CFA compression algorithms.
A New EDI-based Deinterlacing Algorithm

In this paper, we propose a new deinterlacing algorithm using edge direction field, edge parity, and motion expansion scheme. The algorithm consists of an EDI (edge dependent interpolation)-based intra-field deinterlacing and inter-field deinterlacing that uses block-based motion detection. Most of the EDI algorithms use pixel-by-pixel or block-by-block distance to estimate the edge direction, which results in many annoying artifacts. We propose the edge direction field, and estimate an interpolation direction using the field and SAD (sum of absolute differences) values. The edge direction field is a set of edge orientations and their gradient magnitudes. The proposed algorithm assumes that a local minimum around the gradient edge field is most probably the true edge direction. Our approach provides good visual results in various kinds of edges (horizontal, narrow and weak). And we propose a new temporal interpolation method based on block motion detection. The algorithm works reliably in scenes which have very fast moving objects and low SNR signals. Experimental results on various data set show that the proposed algorithm works well for the diverse kinds of sequences and reconstructs flicker-free details in the static region1.
A mobile spherical mosaic system

This paper proposes a new mobile mosaic system using the spherical surface for the panoramic image synthesis. The proposed system consists of image acquisition interface, exposure compensation, local image alignment, spherical projection, and blending. The image acquisition interface guides the users to capture images by displaying a wire frame on the spherical surface. The camera pose and direction are estimated by Gyro sensor and accelerometer. We correct the different brightness levels of images using local means and variances in the overlapped regions. Then, the images are locally realigned since the sensor information has much noise. We implement the successive template matching on the spherical surface, which reduces the sensor errors and misalignment in the 3-D rotational motion. After getting the 3-D rotation information of images, the images are rotated in the virtual 3-D space and projected on the spherical surface. We derive the spherical projection using the radius of virtual mosaic sphere and focal length. Finally, the overlapped multiple images are blended only at the boundaries on the spherical surface. We have implemented the system in the usual tablet PC and mobile phone. According to the various experiments, the proposed spherical mosaic system composes the full environment around the user in real-time.
Warped discrete cosine transform and its application in image compression

This paper introduces the concept of warped discrete cosine transform (WDCT) and an image compression algorithm based on the WDCT. The proposed WDCT is a cascade connection of a conventional DCT and all-pass filters whose parameters can be adjusted to provide frequency warping. Because only the first-order all-pass filters are considered, the WDCT can be implemented by a Laguerre network connected with the DCT. For the more efficient software implementation, we propose truncated and approximated FIR filter banks which can be used instead of the Laguerre network. As a result, the input-output relationship of the WDCT can be represented by a single matrix-vector multiplication, like the DCT. In the proposed image-compression scheme, the frequency response of the all-pass filter is controlled by a fixed set of parameters from which a specified warping parameter is used for a specified frequency range. Also, for each parameter, the corresponding WDCT matrices are computed a priori. For each image block, the best parameter is chosen from the set and the index is sent to the decoder as side information along with the result of corresponding WDCT matrix computation. At the decoder, an inverse WDCT is performed to reconstruct the image. The WDCT based compression outperforms the DCT based compression, for high bit rate applications and for images with high-frequency components. It results in 1.1-3.1-dB PSNR gain over conventional DCT at 1.5 bpp for natural images, and provides more gain for compound images with texts.
Automatic defect classification using boosting

This paper deals with automatic defect classification (ADC) in semiconductor fabrication. The defects such as particle and scratch are automatically classified using a boosting approach. The boosting scheme is based on the Kullback-Leibler distance and linear projection along feature vectors. The paper generates the linear features which discriminate the defects maximally. The features are the linear combinations of Haar-like patterns in the frequency domain. By learning in a boosting manner, the particle and scratch are recognized out of the other defects. And, we propose another feature in the spatial domain which is based on the orientation histogram in the local region. The spatial feature is combined with frequency domain features in a boosting manner. According to the experiments with various defect samples, the accuracy of defect classification is larger than 92% on the average, and scratch is especially recognized with 98% purity. More improvement is expected by the new spatial features such as defect colors, shapes, textures, and so on.
Stochastic Approach to Separate Diffuse and Specular Reflections

This paper presents separation of specular and diffuse reflection components from an image pair. The proposed approach is based on the dichromatic reflectance model and Markov random field models. The proposed method estimates specular and diffuse components by minimizing observation color noise and prior potential of specular reflectance. The specular reflection component is modelled as an MRF and estimated in maximum a posteriori framework. This paper proposes likelihood term of specular component and the prior model of specular reflectance based on Phong's shading. Some experiments show that the proposed approach separates the specular and diffuse reflection components effectively. The separated specular components can be utilized in image-based lighting which renders a scene with virtual lighting sources.
Composition of a Dewarped and Enhanced Document Image From Two View Images

In this paper, we propose an algorithm to compose a geometrically dewarped and visually enhanced image from two document images taken by a digital camera at different angles. Unlike the conventional works that require special equipments or assumptions on the contents of books or complicated image acquisition steps, we estimate the unfolded book or document surface from the corresponding points between two images. For this purpose, the surface and camera matrices are estimated using structure reconstruction, 3-D projection analysis, and random sample consensus-based curve fitting with the cylindrical surface model. Because we do not need any assumption on the contents of books, the proposed method can be applied not only to optical character recognition (OCR), but also to the high-quality digitization of pictures in documents. In addition to the dewarping for a structurally better image, image mosaic is also performed for further improving the visual quality. By finding better parts of images (with less out of focus blur and/or without specular reflections) from either of views, we compose a better image by stitching and blending them. These processes are formulated as energy minimization problems that can be solved using a graph cut method. Experiments on many kinds of book or document images show that the proposed algorithm robustly works and yields visually pleasing results. Also, the OCR rate of the resulting image is comparable to that of document images from a flatbed scanner.
Design of Interchannel MRF Model for Probabilistic Multichannel Image Processing

In this paper, we present a novel framework that exploits an informative reference channel in the processing of another channel. We formulate the problem as a maximum a posteriori estimation problem considering a reference channel and develop a probabilistic model encoding the interchannel correlations based on Markov random fields. Interestingly, the proposed formulation results in an image-specific and region-specific linear filter for each site. The strength of filter response can also be controlled in order to transfer the structural information of a channel to the others. Experimental results on satellite image fusion and chrominance image interpolation with denoising show that our method provides improved subjective and objective performance compared with conventional approaches.
Efficient Unwrap Representation of Faces for Video Editing

Unwrap mosaic is a method for decomposing a video into a 2D texture and a dense mapping that enable the reconstruction of the video from the texture. This representation is useful in some frameworks because we can edit videos by simply retouching 2D textures. However, the complexity of conventional approaches is too high to be adopted in time-critical applications (it takes up to several hours). In this letter, we focus on face-related applications such as face editing and replacement, and propose a face unwrap approach for these applications. To be precise, we adopt the view-based active appearance model (AAM) trackers and estimate dense mappings from the AAM results. The AAM also provides pose information which is also exploited in building the texture map. Experimental results show that our method is very efficient compared with the conventional unwrap mosaic approach. Moreover, based on the proposed system, we develop face-related applications.
A dehazing algorithm using dark channel prior and contrast enhancement

This paper proposes a dehazing algorithm based on dark channel prior and contrast enhancement approaches. The conventional dark channel prior method removes haze and thus restores colors of objects in the scene, but it does not consider the enhancement of image contrast. On the contrary, the image contrast method improves the local contrast of objects, but the colors are often distorted due to the over-stretching of contrast. The proposed algorithm combines the advantages of these two conventional approaches for keeping the color while dehazing. For this, an optimization function is proposed to balance between the contrast and colors distortion, where the contrast measure follows the conventional image statistics and the hue component is used to constrain the color changes. According to the experimental results, the proposed approach compensates for the disadvantages of conventional methods, and enhances contrast with less color distortion.
Disparity estimation using color coherence and stochastic diffusion

This paper deals with disparity estimation based on Markov random field (MRF) models and color coherence. The disparity and line fields are explicitly modeled as MRFs, and are estimated by the stochastic diffusion. The potential functions are defined from the novel stochastic models between disparity and line fields. The color information is also utilized to model the textureless regions where the disparity estimation generally fails. And, the derived potential functions are minimized by the novel energy minimization method called stochastic diffusion. The stochastic diffusion diffuses the potential space using the probability distribution of neighboring fields, and searches for the optimal fields in the converged potential space. Some experiments show good performances of disparity estimation, which are compared with the other methods in a Webpage.
Interpolation of Multi-Spectral Images Inwavelet Domain for Satellite Image Fusion

This paper presents an image interpolation technique for satellite image fusion in the wavelet domain. For the fusion of satellite images, we need to interpolate and match the low resolution multi-spectral images (MSIs) to the high resolution panchromatic images. But since the typical spline-based interpolation methods employed in the conventional image fusion entails blurriness of edges, we propose a wavelet-domain image interpolation method that creates high frequency details based on the estimation of non-existent higher band coefficients from the relationship of available coefficients in lower scales. We model the relationship of coefficients in vertical as well as horizontal direction by the Markov stochastic model, and also find the coefficients of higher scale in this respect. The estimated coefficients are further refined by adding the maximum a posteriori (MAP) estimation process. The proposed interpolation technique is employed into the most popular image fusion algorithms, namely wavelet, principle component analysis (PCA), and intensity-hue-saturation (IHS) transformation based algorithms, instead of the conventional bilinear or bicubic interpolation methods. The experimental results show that the fused image based on the proposed interpolation instead of the conventional bilinear and bicubic interpolation algorithms employed in the conventional wavelet, principle component analysis (PCA), and intensity-hue-saturation (IHS) transformation based fusion algorithms, we incorporate the proposed wavelet based interpolation method.
MAP-MRF approach for binarization of degraded document image

We propose an algorithm for the binarization of document images degraded by uneven light distribution, based on the Markov Random Field modeling with Maximum A Posteriori probability (MAP-MRF) estimation. While the conventional algorithms use the decision based on the thresholding, the proposed algorithm makes a soft decision based on the probabilistic model. To work with the MAP-MRF framework we formulate an energy function by a likelihood model and a generalized Potts prior model. Then we construct a graph for the energy, and obtain the optimized result by using the well-known graph cut algorithm. Experimental results show that our approach is more robust to various types of images than the previous hard decision approaches.
Bayesian Image Interpolation Based on the Learning and Estimation of Higher Bandwavelet Coefficients

This paper presents an image interpolation algorithm based on the estimation of higher band wavelet coefficients. We presume that a given image is the LL band of the wavelet coefficients of a high resolution image that does not actually exist and is target of the interpolation. The proposed method estimates the higher band coefficients by learning the correlation of coefficients across the scale. According to the wavelet theory, a sequence of wavelet coefficients has extreme at the point that corresponds to the singularity of signal, and the extremes across the wavelet scale have some relationship. The main point of the wavelet domain interpolation is to exploit these properties of wavelet coefficients for estimating the extreme points in the higher frequency bands. In this paper, the relationship between the wavelet coefficients across the scale is described by Markov stochastic model, and each wavelet coefficient is modeled by Gaussian mixture that has multiple means and variances. For the enhanced subjective quality of interpolated image through the above modeling, we added refinement process using maximum a posteriori (MAP) technique. Comparison with the existing wavelet-domain and edge-preserving interpolation algorithms shows that the proposed method provides improved objective and subjective quality.
Prior Model for the MRF Modeling of Multi-Channel Images

In multi-channel images (e.g. color images with R, G, B channel, and multi-spectral images), there exist higher-order correlations among the channels. We develop a new MRP-MAP (Markov random field - maximum a posteriori) framework that can be used for various multi-channel image processing. Main features of the proposed framework is that the higher-order correlation between the channels is considered, whereas it is not well addressed in the conventional works. Given a channel image, the prior probability of another channel is computed based on the MRF modeling that the channel correlation is described as piecewise linear relationship. An optimization algorithm for the MAP estimation is also developed. The effectiveness of the proposed priors is demonstrated with a simple application, i.e., image denoising.
Watermarking for 3D NURBS graphic data

In this paper, two watermarking algorithms for nonuniform rational b-spline (NURBS) are proposed. One is suitable for steganography, and the other for watermarking. Both algorithms do not directly embed data into the parameters of NURBS, but into the 2D virtual images extracted from the sampled points of 3D model. As a result, the proposed algorithm for steganography preserves the data size of the model and can embed more information than the conventional algorithm. Also, the watermarking algorithm can use any of existing 2D watermarking techniques when embedding data into the virtual 2D images. From the experiment, it is found that the algorithm is robust against the operations to knots and control points. It is also robust against the remodeling of NURBS model through approximation.
Quantization based reliable equalization algorithm for turbo DFE and dual-mode blind equalizers

In this paper we propose a new adaptive equalization technique for Turbo Decision Feedback Equalizers (TDFE) and dual-mode blind equalizers, that takes the advantage of quantization in the probabilistic scheme. In generating the error signal the proposed technique considers the likelihood between the quantized equalizer output and all the constellation points, thus making the receiver insensitive to the decoding errors. The proposed technique achieves lower BER with less computations than other TDFE algorithms. Also a more reliable soft transition between start-up and Decision Directed (DD) mode in blind equalizers is proposed. This technique has been compared with Benveniste-Goursat, Stop-and-Go and Lim's soft transition algorithm. It is shown that the proposed approach is superior and more robust under different channel and SNR conditions.
Image-adaptive watermarking based on warped discrete cosine transform

For effective watermarking, the majority of watermarking schemes have been developed to meet two basic requirements: robustness and transparency, which conflict with each other from a signal processing perspective. In this paper, we propose an image-adaptive watermarking algorithm for digital images, based on the warped discrete cosine transform (WDCT) and visual models. The WDCT has been developed in the context of image compression, however, the WDCT is utilized for an image-adaptive digital watermarking scheme to enhance transparency and robustness, while preserving the visual quality of the images. Also the visual models are adapted to determine image dependent upper bounds on an embedded watermark. Through intensive computer simulations, it is shown that the proposed scheme yields more effective and better performance, both in terms of transparency and robustness, than the conventional algorithms.
Gaze estimation using 3-D eyeball model and eyelid shapes

This paper proposes a gaze estimation algorithm using 3-D eyeball model and eyelid shape. The gaze estimation suffers from differences of eye shapes and individual behaviors, and requires user-specific gaze calibration. The proposed method exploits the usual 3-D eyeball model and shapes of the eyelid to estimate gaze without user-specific calibration and learning. Since the gaze is closely related to the 3-D rotation of eyeball, this paper first derives the relation between 2-D pupil location extracted in the eye image and 3-D rotation of eyeball. This paper also models the shapes of the eyelid to adjust gaze based on the observation that the shapes of the eyelid are deformed with respect to the gaze. This paper models the curvature of eyelid curve to compensate for the gaze. According to the various experiments, the proposed method shows good results in gaze estimation. The proposed method does not need user-specific calibration or gaze learning since the general 3-D eyeball and eyelid models are exploited in the localized eye region. Therefore, it is expected that the proposed gaze estimation algorithm is suitable for various applications such as VR/AR devices, driver gaze tracking, gaze-based interfaces, and so on.
A new image denoising method based on thewavelet domain nonlocal means filtering

We present a new image denoising method based on the nonlocal means filtering in the wavelet domain. A noisy image is first decomposed into subbands by wavelet transform and the nonlocal means filter is applied to each subband. It is also noted that the performance of the nonlocal means filter depends on the kernel bandwidth (size of the filter) and the image properties. Hence we propose a method to adjust the kernel bandwidth for each of the subband images, based on the estimation of noise statistics. This filtering method preserves the wavelet coefficients corresponding to the structures, while effectively suppressing noisy ones. Experimental results show that the proposed method provides comparable or sometimes higher peak signal-to-noise ratio (PSNR) than the state-of-the-art wavelet denoising methods and the spatial nonlocal means filter. Subjective comparison also shows that the proposed method provides better contrast than the spatial nonlocal means filter, and less ringing artifacts that commonly arise in the conventional wavelet denoising.