Example-based image super-resolution with class-specific predictors

Example-based super-resolution is a promising approach to solving the image super-resolution problem. However, the learning process can be slow and prediction can be inaccurate. In this paper, we present a novel learning-based algorithm for image super-resolution to improve the computational speed and prediction accuracy. Our new method classifies image patches into several classes, for each class, a class-specific predictor is designed. A class-specific predictor takes a low-resolution image patch as input and predicts a corresponding high-resolution patch as output. The performances of the class-specific predictors are evaluated using different datasets formed by face images and natural-scene images. We present experimental results which demonstrate that the new method provides improved performances over existing methods.     

Accurate image registration for MAP image super-resolution

The accuracy of image registration plays a dominant role in image super-resolution methods and in the related literature, landmark-based registration methods have gained increasing acceptance in this framework. In this work, we take advantage of a maximum a posteriori (MAP) scheme for image super-resolution in conjunction with the maximization of mutual information to improve image registration for super-resolution imaging. Local as well as global motion in the low-resolution images is considered. The overall scheme consists of two steps. At first, the low-resolution images are registered by establishing correspondences between image features. The second step is to fine-tune the registration parameters along with the high-resolution image estimation, using the maximization of mutual information criterion. Quantitative and qualitative results are reported indicating the effectiveness of the proposed scheme, which is evaluated with different image features and MAP image super-resolution computation methods.     

An improved regularization method for artifact rejection in image super-resolution

The main aim of this paper is to employ an improved regularization method to super-resolution problems. Super-resolution refers to a process that increases spatial resolution by fusing information from a sequence of images acquired in one or more of several possible ways. This process is an inverse problem, one that is known to be highly ill-conditioned. Total Variation regularization is one of the well-known techniques used to deal with such problems, which has some disadvantages like staircase effect artifacts and nonphysical dissipation. To enhance the robustness of processing against these artifacts, this paper proposes a new regularization method based on the coupling of fourth order PDE and a type of newly designed shock filtering based on complex diffusion in addition to previous Total Variation. In order to have sharp corner structures like edges, this work also considers a corner shock filter. The proposed scheme is not only able to remove the jittering effect artifacts along the edge directions but also able to restrain the rounding artifacts around the corner structures and most importantly, the stabilization of the overall process is assured.     

Solving a two-colour problem by applying probabilistic approach to a full-colour multi-frame image super-resolution

Reconstruction based algorithms play an important role in the multi-frame super-resolution problem. A group of images of the same scene are fused together to produce an image with higher spatial resolution, or with more visible details in the high spatial frequency features. Demosaicing algorithms interpolate missing pixels in a raw image taken from one Charged Coupled Device (CCD) array, upsampling the number of the pixels present in the image. Since super-resolution (SR) and demosaicing are the two faces of the same problem it is natural to address them together. In this paper it is: (i) shown that correct modelling of the Bayer pattern in the generative process improves the super-resolution performance for colour images, and (ii) an algorithm that incorporates the two colour prior into the probabilistic model is designed. The algorithm presented in this paper focuses on the classes of images that have two dominant colours, i.e. most of the areas in the image are uniformly coloured. A convex optimization procedure for joint super-resolution and demosaicing is developed which outperforms state-of-the-art algorithms.     

一种改进的退化文本图像二值化方法

文本图像二值化是文本图像识别的重要步骤,由于光照不均或文档水渍等原因导致文本图像退化,增加了文本图像识别的难度。本文对一种局部阈值算法进行了改进,首先对图像进行水平投影,根据直方图的极小点对版面进行简单划分,再利用全局阈值法估算出更为准确的各区域字符笔画宽度,从而自适应地得到适当的窗口尺寸,再利用对比图和局部阈值进行图像二值化,并结合OTSU图像消除原算法产生的伪轮廓。实验与分析表明,改进后的方法能够明显消除因笔画粗细不均、字符大小不同而产生的前景像素误识问题。     

Spatiotemporal approach for time-varying global image motionestimation

Image motion estimation using the spatiotemporal approach has largely relied on the constant velocity assumption, and thus becomes inappropriate when the velocity of the imaged scene or the camera changes during the data acquisition time. Using a polynomial or a trigonometric polynomial model for the time variation of the image motion, spatiotemporal algorithms are developed in this paper to handle time-varying (but space-invariant) motion. Under these models, it is shown that time-varying image motion estimation is equivalent to parameter estimation of one-dimensional (1-D) polynomial phase or phase-modulated signals, which allows one to exploit well-established results in radar signal processing. When compared with alternative approaches, the resulting motion estimation algorithms produce more accurate estimates. Simulation results are provided to demonstrate the proposed schemes.     

从单幅图像恢复立体景象的新方法

在研究单幅平面图像内在特性的基础上,提出了一种恢复立体视觉景象建模的新方法。对图像进行智能识别处理,可以求得许多线段的特征参数,并由此计算出消隐点和消隐线,从而可自动获得场景的立体结构信息。本算法的特点在于用一个代数表达式统一了三种典型的度量方法,无需传统的相机内校正参数,直接可计算出建模用立体信息。建模结果用VRML格式保存、输出,以便于网上浏览。众多的图像验证了该方法的有效性、适用性。     

DPA: a deterministic approach to the MAP problem

Deterministic pseudo-annealing (DPA) is a new deterministic optimization method for finding the maximum a posteriori (MAP) labeling in a Markov random field, in which the probability of a tentative labeling is extended to a merit function on continuous labelings. This function is made convex by changing its definition domain. This unambiguous maximization problem is solved, and the solution is followed down to the original domain, yielding a good, if suboptimal, solution to the original labeling assignment problem. The performance of DPA is analyzed on randomly weighted graphs.     

Single sideband FSS with high image rejection

The design of a quasi-optical single sideband filter, which provides more than 30 dB of isolation between the frequency bands 294-305.5 and 329.5-341.5 GHz in the TM plane at 45deg incidence, is described. The structure, which consists of three free-standing arrays of dipole slot elements, generates a bandpass spectral response with an insertion loss below 0.5 dB at resonance. Simulated and measured transmission coefficients in the range 250-400 GHz are shown to be in good agreement     

A low-complexity image compression approach with single spatial prediction mode and transform

The well-known low-complexity JPEG and the newer JPEG-XR systems are based on block-based transform and simple transform-domain coefficient prediction algorithms. Higher complexity image compression algorithms, obtainable from intra-frame coding tools of video coders H.264 or HEVC, are based on multiple block-based spatial-domain prediction modes and transforms. This paper explores an alternative low-complexity image compression approach based on a single spatial-domain prediction mode and transform, which are designed based on a global image model. In our experiments, the proposed single-mode approach uses an average 20.5 % lower bit-rate than a standard low-complexity single-mode image coder that uses only conventional DC spatial prediction and 2-D DCT. It also does not suffer from blocking effects at low bit-rates.     

A modified aperture-synthesis method for super-resolution by a single digital hologram

A modified aperture-synthesis method is reported to improve the resolution of a reconstructed image in digital holography by a single hologram. A series of sub-holograms incoherently overlapped on a CCD are recorded as a single aperture-synthesized digital hologram. The angular division multiplexing is introduced to both the object and the reference path for holographic recording. The final super-resolved image is obtained by synthesizing the several reconstructed images. In the experiment, a two-dimensional transparent USAF resolution test target is used. The result demonstrates that the resolution of the reconstructed image is improved from 16.00 lp/mm to 22.64 lp/mm in the horizontal direction.     

Anisotropic nonlinear diffusion approach to single image resolution enhancement

In many image resolution enhancement applications, the blurring process of the imaging system is unknown. This paper discusses the problem of single image blind resolution enhancement without estimating the point spread function （PSF）. A regularization model is constructed for image enhancement based on the prior informaton of image error and image gray value,which does not need any prior in- formation of PSF. Moreover, through the solution of Euler equations, an an isotropic nonlinear diffusion equation are obtained,which can avoid the high computational cost of regularization model. Furthermore,in order to get sub-pixel superresolved image, the regularization model for image enhancement is extended to the enlargement of image, which can enlarge and enhance image at the same time. Last,to get clearer edges, a high frequency enhancement filter is used on the superresolved image. Numerical results show that the new model can get much clearer super-resolution images than traditional methods, and the peak signal to noise ratios （PSNRs） are also higher than traditional methods.     

Single frame image super-resolution: should we process locally or globally?

In this paper we study the usefulness of different local and global, learning-based, single-frame image super-resolution reconstruction techniques in handling three specific tasks, namely, de-blurring, de-noising and alias removal. We start with the global, iterative Papoulis–Gerchberg method for super-resolving a scene. Next we describe a PCA-based global method which faithfully reproduces a super-resolved image from a blurred and noisy low resolution input. We also study several multi-resolution processing schemes for super-resolution where the best edges are learned locally from an image database. We show that the PCA-based global method is efficient in handling blur and noise in the data. The local methods are adept in capturing the edges properly. However, both local and global approaches cannot properly handle the aliasing present in the low resolution observation. Hence we propose an alias removal technique by designing an alias-free upsampling scheme. Here the unknown high frequency components of the given partially aliased (low resolution) image is generated by minimizing the total variation of the interpolant subject to the constraint that part of alias free spectral components in the low resolution observation are known precisely and under the assumption of sparsity in the data.     

Absolute phase image reconstruction: a stochastic nonlinearfiltering approach

This paper formulates and proposes solutions to the problem of estimating/reconstructing the absolute (not simply modulo-2pi) phase of a complex random field from noisy observations of its real and imaginary parts. This problem is representative of a class of important imaging techniques such as interferometric synthetic aperture radar, optical interferometry, magnetic resonance imaging, and diffraction tomography. We follow a Bayesian approach; then, not only a probabilistic model of the observation mechanism, but also prior knowledge concerning the (phase) image to be reconstructed, are needed. We take as prior a nonsymmetrical half plane autoregressive (NSHP AR) Gauss-Markov random field (GMRF). Based on a reduced order state-space formulation of the (linear) NSHP AR model and on the (nonlinear) observation mechanism, a recursive stochastic nonlinear filter is derived, The corresponding estimates are compared with those obtained by the extended Kalman-Bucy filter, a classical linearizing approach to the same problem. A set of examples illustrate the effectiveness of the proposed approach.     

A 5.2-GHz CMOS receiver with 62-dB image rejection

A 5.2-GHz CMOS receiver employs a double downconversion heterodyne architecture with a local oscillator frequency of 2.6 GHz and applies offset cancellation to the baseband amplifiers. Placing the image around the zero frequency, the receiver achieves an image rejection of 62 dB with no external components while minimizing the flicker noise upconversion in the first mixing operation. Realized in a 0.25-μm digital CMOS technology, the circuit exhibits a noise figure of 6.4 dB, an IP₃ of -15 dBm, and a voltage conversion gain of 43 dB, while draining 29 mW from a 2.5-V supply     

单幅图像的快速去雾算法

雾的存在使得户外图像的处理变得困难。雾、霭、烟等现象会使彩色图像退化,对比度降低。介绍了一种单幅图像的去雾新算法,不需要分割图像,直接利用高斯低通滤波器分离出背景空气光,利用改良的暗通道法对大气光进行估计,结合雾天图像的物理模型对图像进行复原,最后再对图像的饱和度进行校正,得到最终的复原效果。该算法的主要优点是速度快,...     

A global approach for solving evolutive heat transfer for image denoising and inpainting.

This paper proposes an alternative to partial differential equations (PDEs) for solving problems in computer vision based on evolutive heat transfer. Traditionally, the method for solving such physics-based problems is to discretize and solve a PDE by a purely mathematical process. Instead of using the PDE, we propose to use the global heat principle and to decompose it into basic laws. We show that some of these laws admit an exact global version since they arise from conservative principles.We also show that the assumptions made about the other basic Iaws can be made wisely, taking into account knowledge about the problem and the domain. The numerical scheme is derived in a straightforward way from the modeled problem, thus providing a physical explanation for each step in the solution. The advantage of such an approach is that it minimizes the approximations made during the whole process and it modularizes it, allowing changing the application to a great number of problems. We apply the scheme to two applications: image denoising and inpainting which are modeled with heat transfer. For denoising, we propose a new approximation for the conductivity coefficient and we add thin lines to the features in order to block diffusion.