羟氨苄青霉素引起大疱性类天疱疮样疹

A fast and effective image fusion method is proposed for creating a highly informative fused image through merging multiple images. The proposed method is based on a two-scale decomposition of an image into a base layer containing large scale variations in intensity, and a detail layer capturing small scale details. A novel guided filtering-based weighted average technique is proposed to make full use of spatial consistency for fusion of the base and detail layers. Experimental results demonstrate that the proposed method can obtain state-of-the-art performance for fusion of multispectral, multifocus, multimodal, and multiexposure images.

/pdf/image-fusion-with-guided-filtering-4bhdh8vaii.pdf

Image Fusion With Guided Filtering

Pixel-level image fusion

IFCNN: A general image fusion framework based on convolutional neural network

Due to the poor lighting condition and limited dynamic range of digital imaging devices, the recorded images are often under-/over-exposed and with low contrast. Most of previous single image contrast enhancement (SICE) methods adjust the tone curve to correct the contrast of an input image. Those methods, however, often fail in revealing image details because of the limited information in a single image. On the other hand, the SICE task can be better accomplished if we can learn extra information from appropriately collected training data. In this paper, we propose to use the convolutional neural network (CNN) to train a SICE enhancer. One key issue is how to construct a training data set of low-contrast and high-contrast image pairs for end-to-end CNN learning. To this end, we build a large-scale multi-exposure image data set, which contains 589 elaborately selected high-resolution multi-exposure sequences with 4,413 images. Thirteen representative multi-exposure image fusion and stack-based high dynamic range imaging algorithms are employed to generate the contrast enhanced images for each sequence, and subjective experiments are conducted to screen the best quality one as the reference image of each scene. With the constructed data set, a CNN can be easily trained as the SICE enhancer to improve the contrast of an under-/over-exposure image. Experimental results demonstrate the advantages of our method over existing SICE methods with a significant margin.

Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images

A single captured image of a real-world scene is usually insufficient to reveal all the details due to under- or over-exposed regions To solve this problem, images of the same scene can be first captured under different exposure settings and then combined into a single image using image fusion techniques In this paper, we propose a novel probabilistic model-based fusion technique for multi-exposure images Unlike previous multi-exposure fusion methods, our method aims to achieve an optimal balance between two quality measures, ie, local contrast and color consistency, while combining the scene details revealed under different exposures A generalized random walks framework is proposed to calculate a globally optimal solution subject to the two quality measures by formulating the fusion problem as probability estimation Experiments demonstrate that our algorithm generates high-quality images at low computational cost Comparisons with a number of other techniques show that our method generates better results in most cases

Generalized Random Walks for Fusion of Multi-Exposure Images

Many state-of-the-art fusion methods, combining details in images taken under different exposures into one well-exposed image, can be found in the literature. However, insufficient study has been conducted to explore how perceptual factors can provide viewers better quality of experience on fused images. We propose two perceptual quality measures: perceived local contrast and color saturation, which are embedded in our novel hierarchical multivariate Gaussian conditional random field model, to illustrate improved performance for multi-exposure fusion. We show that our method generates images with better quality than existing methods for a variety of scenes.

QoE-Based Multi-Exposure Fusion in Hierarchical Multivariate Gaussian CRF

With the boost of computing power in mobile devices and availability of cloud APIs in recent years, mobile augmented reality (AR) applications have become increasingly embedded in people's everyday life. However, effective and intuitive interaction between virtual and real worlds in these AR applications is still an open question. In this paper, we make one step towards an answer by exploring the possibility of incorporating two input modalities, gesture and speech, for enhancing user experience in AR applications.

Multimodal interaction in augmented reality

This paper presents a novel two-phase stereo matching algorithm using the random walks framework. At first, a set of reliable matching pixels is extracted with prior matrices defined on the penalties of different disparity configurations and Laplacian matrices defined on the neighbourhood information of pixels. Following this, using the reliable set as seeds, the disparities of unreliable regions are determined by solving a Dirichlet problem. The variance of illumination across different images is taken into account when building the prior matrices and the Laplacian matrices, which improves the accuracy of the resulting disparity maps. Even though random walks have been used in other applications, our work is the first application of random walks in stereo matching. The proposed algorithm demonstrates good performance using the Middlebury stereo datasets.

/pdf/stereo-matching-using-random-walks-4s01vgsp2q.pdf

Stereo matching using random walks

The use of multimedia in learning and testing has been gaining popularity over the last few years. In this paper, we describe a method for estimating difficulty level of a multimedia mathematical item type using Item Response Theory (IRT). Experimental results on determining the parameters of an IRT model through tests on a group of students are also presented. Linear regression equations are fitted based on test data collected on a group of students. Results show a high degree of correlation between observed difficulty levels and fitted values.

An Algorithm for Automatic Difficulty Level Estimation of Multimedia Mathematical Test Items

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