SciCombinator

Discover the most talked about and latest scientific content & concepts.

Journal: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

29

Many digital imaging devices operate by successive photon-to-electron, electron-to-voltage, and voltage-to-digit conversions. These processes are subject to various signal-dependent errors, which are typically modeled as Poisson-Gaussian noise. The removal of such noise can be effected indirectly by applying a variance-stabilizing transformation (VST) to the noisy data, denoising the stabilized data with a Gaussian denoising algorithm, and finally applying an inverse VST to the denoised data. The generalized Anscombe transformation (GAT) is often used for variance stabilization, but its unbiased inverse transformation has not been rigorously studied in the past. We introduce the exact unbiased inverse of the GAT and show that it plays an integral part in ensuring accurate denoising results. We demonstrate that this exact inverse leads to state-of-the-art results without any notable increase in the computational complexity compared to the other inverses. We also show that this inverse is optimal in the sense that it can be interpreted as a maximum likelihood inverse. Moreover, we thoroughly analyze the behavior of the proposed inverse, which also enables us to derive a closed-form approximation for it. This paper generalizes our work on the exact unbiased inverse of the Anscombe transformation, which we have presented earlier for the removal of pure Poisson noise.

Concepts: Variance-stabilizing transformation, Maximum likelihood, Data analysis, Anscombe transform, Inverse function, Computational complexity theory, Normal distribution, Poisson distribution

28

Single sensor digital cameras use color filter arrays to capture a subset of the color data at each pixel coordinate. Demosaicing or color filter array (CFA) interpolation is the process of estimating the missing color samples to reconstruct a full color image. In this paper, we propose a demosaicing method that uses multiscale color gradients to adaptively combine color difference estimates from different directions. The proposed solution does not require any thresholds since it does not make any hard decisions, and it is noniterative. Although most suitable for the Bayer CFA pattern, the method can be extended to other mosaic patterns. To demonstrate this, we describe its application to the Lukac CFA pattern. Experimental results show that it outperforms other available demosaicing methods by a clear margin in terms of CPSNR and S-CIELAB measures for both mosaic patterns.

Concepts: Raw image format, Digital camera, Color, Demosaicing, Digital photography, Color filter array, Mathematics, Image sensor

28

This brief proposes a continuously-valued Markov random field (MRF) model with separable filter bank, denoted as MRFSepa, which significantly reduces the computational complexity in the MRF modeling. In this framework, we design a novel gradient-based discriminative learning method to learn the potential functions and separable filter banks. We learn MRFSepa models with 2-D and 3-D separable filter banks for the applications of gray-scale/color image denoising and color image demosaicing. By implementing MRFSepa model on graphics processing unit, we achieve real-time image denoising and fast image demosaicing with high-quality results.

Concepts: Probability theory, Graphic design, Vision, Digital photography, Model, 3D computer graphics, Computational complexity theory, Computer graphics

28

Recently, there has been significant interest in robust fractal image coding for the purpose of robustness against outliers. However, the known robust fractal coding methods (HFIC and LAD-FIC, etc.) are not optimal, since, besides the high computational cost, they use the corrupted domain block as the independent variable in the robust regression model, which may adversely affect the robust estimator to calculate the fractal parameters (depending on the noise level). This paper presents a Huber fitting plane-based fractal image coding (HFPFIC) method. This method builds Huber fitting planes (HFPs) for the domain and range blocks, respectively, ensuring the use of an uncorrupted independent variable in the robust model. On this basis, a new matching error function is introduced to robustly evaluate the best scaling factor. Meanwhile, a median absolute deviation (MAD) about the median decomposition criterion is proposed to achieve fast adaptive quadtree partitioning for the image corrupted by salt & pepper noise. In order to reduce computational cost, the no-search method is applied to speedup the encoding process. Experimental results show that the proposed HFPFIC can yield superior performance over conventional robust fractal image coding methods in encoding speed and the quality of the restored image. Furthermore, the no-search method can significantly reduce encoding time and achieve less than 2.0 s for the HFPFIC with acceptable image quality degradation. In addition, we show that, combined with the MAD decomposition scheme, the HFP technique used as a robust method can further reduce the encoding time while maintaining image quality.

Concepts: Errors and residuals in statistics, Standard deviation, Regression analysis, Absolute deviation, Function, Median, Median absolute deviation, Robust statistics

28

Light emitting diode (LED)-backlit liquid crystal displays (LCDs) hold the promise of improving image quality while reducing the energy consumption with signal-dependent local dimming. However, most existing local dimming algorithms are mostly motivated by simple implementation, and they often lack concern for visual quality. To fully realize the potential of LED-backlit LCDs and reduce the artifacts that often occur in current systems, we propose a novel local dimming technique that can achieve the theoretical highest fidelity of intensity reproduction in either l(1) or l(2) metrics. Both the exact and fast approximate versions of the optimal local dimming algorithm are proposed. Simulation results demonstrate superior performances of the proposed algorithm in terms of visual quality and power consumption.

Concepts: Gamut, Energy, Cathode ray tube, Liquid crystal, Backlight, Lighting, Light-emitting diode, Liquid crystal display

28

This paper adapts the least-squares luma-chroma demultiplexing (LSLCD) demosaicking method to noisy Bayer color filter array (CFA) images. A model is presented for the noise in white-balanced gamma-corrected CFA images. A method to estimate the noise level in each of the red, green, and blue color channels is then developed. Based on the estimated noise parameters, one of a finite set of configurations adapted to a particular level of noise is selected to demosaic the noisy data. The noise-adaptive demosaicking scheme is called LSLCD with noise estimation (LSLCD-NE). Experimental results demonstrate state-of-the-art performance over a wide range of noise levels, with low computational complexity. Many results with several algorithms, noise levels, and images are presented on our companion web site along with software to allow reproduction of our results.

Concepts: Statistics, Estimation, Mathematics, Demosaicing, Computational complexity theory, Digital photography, Bayer filter, Color filter array

28

This paper presents a novel reaction-diffusion (RD) method for implicit active contours that is completely free of the costly reinitialization procedure in level set evolution (LSE). A diffusion term is introduced into LSE, resulting in an RD-LSE equation, from which a piecewise constant solution can be derived. In order to obtain a stable numerical solution from the RD-based LSE, we propose a two-step splitting method to iteratively solve the RD-LSE equation, where we first iterate the LSE equation, then solve the diffusion equation. The second step regularizes the level set function obtained in the first step to ensure stability, and thus the complex and costly reinitialization procedure is completely eliminated from LSE. By successfully applying diffusion to LSE, the RD-LSE model is stable by means of the simple finite difference method, which is very easy to implement. The proposed RD method can be generalized to solve the LSE for both variational level set method and partial differential equation-based level set method. The RD-LSE method shows very good performance on boundary antileakage. The extensive and promising experimental results on synthetic and real images validate the effectiveness of the proposed RD-LSE approach.

Concepts: Finite differences, Mathematics, Finite difference method, Level set, Numerical analysis, Finite difference, Level set method, Partial differential equation

28

This paper proposes a novel local feature descriptor, Local Directional Number Pattern (LDN), for face analysis: face and expression recognition. LDN encodes the directional information of the faces textures (i.e., the textures structure) in a compact way, producing a more discriminative code than current methods. We compute the structure of each micro-pattern with the aid of a compass mask, that extracts directional information, and we encode such information using the prominent direction indexes (directional numbers) and signwhich allows us to distinguish among similar structural patterns that have different intensity transitions. We divide the face into several regions, and extract the distribution of the LDN features from them. Then, we concatenate these features into a feature vector, and we use it as a face descriptor. We perform several experiments in which our descriptor performs consistently under illumination, noise, expression, and time lapse variations. Moreover, we test our descriptor with different masks to analyze its performance in different face analysis tasks.

Concepts: Mask, Structure, Pattern, Small Faces, Rod Stewart, Dean Koontz, Face, Faces

28

Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR). In SRC, the testing image is coded as a sparse linear combination of the training samples, and the representation fidelity is measured by the l2-norm or l1-norm of the coding residual. Such a sparse coding model assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be effective enough to describe the coding residual in practical FR systems. Meanwhile, the sparsity constraint on the coding coefficients makes SRCs computational cost very high. In this paper, we propose a new face coding model, namely regularized robust coding (RRC), which could robustly regress a given signal with regularized regression coefficients. By assuming that the coding residual and the coding coefficient are respectively independent and identically distributed, the RRC seeks for a maximum a posterior solution of the coding problem. An iteratively reweighted regularized robust coding (IR3C) algorithm is proposed to solve the RRC model efficiently. Extensive experiments on representative face databases demonstrate that the RRC is much more effective and efficient than state-of-the-art sparse representation based methods in dealing with face occlusion, corruption, lighting and expression changes, etc.

Concepts: Sparse matrix, Computational complexity theory, Lp space, Proposal, Coefficient, Binomial coefficient, Regression analysis, Vector space

28

Automatic image annotation, which is usually formulated as a multi-label classification problem, is one of major tools to enhance the semantic understanding of web images. Many multimedia applications (e.g., tag-based image retrieval) can greatly benefit from image annotation. However, the insufficient performance of image annotation methods prevents these applications from being practical. On the other hand, specific measures are usually designed to evaluate how well one annotation method performs for specific objective/application, but most of image annotation methods do not consider optimization of these measures, so that they are inevitably trapped into suboptimal performance of these objective specific measures. To address this issue, we first summarize a variety of objectiveguided performance measures under a unified representation. Our analysis reveals that macro-averaging measures are very sensitive to infrequent keywords, and hamming measure is easily affected by skewed distributions.We then propose a unified multilabel learning framework, which directly optimizes a variety of objective specific measures of multi-label learning tasks. Specifically, we first present a multilayer hierarchical structure of learning hypotheses for multi-label problems based on which a variety of loss functions with respect to objective-guided measures are defined. And then, we formulate these loss functions as relaxed surrogate functions and optimize them by structural SVMs. According to the analysis of various measures and the high time complexity of optimizing micro-averaging measures, in this paper, we focus on example-based measures which are tailor-made for image annotation tasks but are seldom explored in the literature. Experiments show the consistence with the formal analysis on two widely used multi-label datasets and demonstrate the superior performance of our proposed method over state-ofthe- art baseline methods in terms of example-based measures on four image annotation datasets.

Concepts: Median, Semantics, Structure, Applications of computer vision, Hierarchy, Image retrieval, Machine learning, Automatic image annotation