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In medical image analysis, multi-modal registration has been a challenging task due to the complex intensity relationship between images to be aligned. Conventional multi-modal approaches tend to assess the accuracy of the alignment by measuring a similarity based on statistical dependency of the intensity values between images. However, measuring statistical similarity measures, such as mutual information,...
We propose a novel approach for pixel classification in hyperspectral images, leveraging on both the spatial and spectral information in the data. The introduced method relies on a recently proposed framework for learning on distributions — by representing them with mean elements in reproducing kernel Hilbert spaces (RKHS) and formulating a classification algorithm therein. In particular, we associate...
Restoring underwater image from a single image is known to be an ill-posed problem. Some assumptions made in previous methods are not suitable in many situations. In this paper, an effective method is proposed to restore underwater images. Using the quad-tree subdivision and graph-based segmentation, the global background light can be robustly estimated. The medium transmission map is estimated based...
High resolution hyper-spectral imaging works as a scheme to obtain images with high spatial and spectral resolutions by merging a low spatial resolution hyper-spectral image (HSI) with a high spatial resolution multi-spectral image (MSI). In this paper, we propose a novel method based on probabilistic matrix factorization under Bayesian framework: First, Gaussian priors, as observations' distributions,...
This paper proposes a new temporal consistency measure for quality assessment of synthesized video. Disocclusion regions appear hole regions of the synthesized video at virtual viewpoints. Filling hole regions could be problematic when the synthesized video is perceived through multi-view displays. In particular, the temporal inconsistency caused by hole filling process in view synthesis could affect...
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The theory of compressed sensing (CS) leverages upon structure of signals in order to reduce the number of samples needed to reconstruct a signal, compared to the Nyquist rate. Although CS approaches have been proposed for ultrasound (US) imaging with promising results, practical implementations are hard to achieve due to the impossibility to mimic random sampling on a US probe and to the high memory...
Video quality assessment can be performed by comparing distorted video with the undistorted version by taking human vision system (HVS) into account. A perceptual vision sensitivity model is developed in this paper by systematically integrating visual attention and foveation mechanism into contrast sensitivity function (CSF). The model can accurately estimate a critical frequency beyond which the...
Annihilating filer-based low rank Hankel matrix (ALOHA) approach was recently proposed as an intrinsic image model for image inpainting estimation. Based on the observation that smoothness or textures within an image patch are represented as sparse spectral components in the frequency domain, ALOHA exploits the existence of annihilating filters and the associated rank-deficient Hankel matrices in...
Brain imaging data such as EEG or MEG is high-dimensional spatiotemporal measurements that commonly require dimensionality reduction before being used for further analysis or applications. This paper presents a new dimensionality reduction method based on the recent graph signal processing theory. Specifically, we focus on a task to classify the brain imaging signals recording the cortical activities...
Imaging through scattering media is a significant challenge in computational imaging. Recently, a breakthrough technique based on speckle scanning was proposed with outstanding imaging performance. However, the dense angular scanning of the incident laser beam leads to a lengthy scanning process. In this paper, we propose a method based on compressive sensing (CS) to accelerate the data acquisition...
Depth profile reconstruction of a scene at low light levels using an active imaging setup has wide-ranging applications in remote sensing. In such low-light imaging scenarios, single-photon detectors are employed to time-resolve individual photon detections. However, even with single-photon detectors, current frameworks are limited to using hundreds of photon detections at each pixel to mitigate Poisson...
With the fast advances in video acquisition, computational imaging, and display technologies, there has been a growing interest in high dynamic range (HDR) videos. Tone mapping operators (TMOs) that convert HDR content to low dynamic range (LDR) ones provide a practically useful solution for the visualization of HDR videos on standard LDR displays, where the user experience highly depends on the performance...
This paper describes an atmospheric lidar photon-limited imaging problem in which observations are contaminated with Poisson noise. The observations are a nonlinear function of two spatially varying physical parameters. The first parameter, called the transmittance, is known to be a bounded monotonic non-increasing function. The second parameter, called the backscatter cross-section, is non-negative...
The High Efficiency Video Coding (HEVC) standard includes support for a large range of image representation formats and provides an excellent image compression capability. The High Efficiency Image File Format (HEIF) offers a convenient way to encapsulate HEVC coded images, image sequences and animations together with associated metadata into a single file. This paper discusses various features and...
Plankton image classification plays an important role in the ocean ecosystems research. Recently, a large scale database for plankton classification with over 3 million images annotated with over 100 classes was released. However, the database suffers from imbalanced class distribution in which over 90% of images belong to only 5 classes. Due to this class-imbalance problem, the existing classification...
This paper proposes using a Gaussian mixture model as a patch-based prior, for solving two image inverse problems, namely image deblurring and compressive imaging. We capitalize on the fact that variable splitting algorithms, like ADMM, are able to decouple the handling of the observation operator from that of the regularizer, and plug a state-of-the-art algorithm into the denoising step. Furthermore,...
The explosion of computational imaging has seen the frontier of image processing move past linear problems, like denoising and deblurring, and towards non-linear problems such as phase retrieval. There has a been a corresponding research thrust into non-linear image recovery algorithms, but in many ways this research is stuck where linear problem research was twenty years ago: Models, if used at all,...
An image taken under extremely low illumination is modeled as obeying the Poissonian-Gaussian distribution. This paper proposes a method to recover a higher-quality color moving-image sequence from Poissonian-Gaussian observations. The method performs virtual multiplex imaging, formed as a series of pixel binning and redundant subsampling, on the input sequence to increase its statistical reliability,...
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