Showing papers on "Image quality published in 2013"

Making a “Completely Blind” Image Quality Analyzer

Abstract: An important aim of research on the blind image quality assessment (IQA) problem is to devise perceptual models that can predict the quality of distorted images with as little prior knowledge of the images or their distortions as possible. Current state-of-the-art “general purpose” no reference (NR) IQA algorithms require knowledge about anticipated distortions in the form of training examples and corresponding human opinion scores. However we have recently derived a blind IQA model that only makes use of measurable deviations from statistical regularities observed in natural images, without training on human-rated distorted images, and, indeed without any exposure to distorted images. Thus, it is “completely blind.” The new IQA model, which we call the Natural Image Quality Evaluator (NIQE) is based on the construction of a “quality aware” collection of statistical features based on a simple and successful space domain natural scene statistic (NSS) model. These features are derived from a corpus of natural, undistorted images. Experimental results show that the new index delivers performance comparable to top performing NR IQA models that require training on large databases of human opinions of distorted images. A software release is available at http://live.ece.utexas.edu/research/quality/niqe_release.zip.

Projective image restoration using sparsity regularization

Abstract: This paper presents a method of image restoration for projective ground images which lie on a projection orthogonal to the camera axis. The ground images are initially transformed using homography, and then the proposed image restoration is applied. The process is performed in the dual-tree complex wavelet transform domain in conjunction with L0 reweighting and L2 minimisation (L0RL2) employed to solve this ill-posed problem. We also propose instant estimation of a blur kernel arising from the projective transform and the subsequent interpolation of sparse data. Subjective results show significant improvement of image quality. Furthermore, classification of surface type at various distances (evaluated using a support vector machine classifier) is also improved for the images restored using our proposed algorithm.

Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index

Abstract: It is an important task to faithfully evaluate the perceptual quality of output images in many applications such as image compression, image restoration and multimedia streaming. A good image quality assessment (IQA) model should not only deliver high quality prediction accuracy but also be computationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD). The image gradients are sensitive to image distortions, while different local structures in a distorted image suffer different degrees of degradations. This motivates us to explore the use of global variation of gradient based local quality map for overall image quality prediction. We find that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted images combined with a novel pooling strategy the standard deviation of the GMS map can predict accurately perceptual image quality. The resulting GMSD algorithm is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy.

Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption

Abstract: Recently, more and more attention is paid to reversible data hiding (RDH) in encrypted images, since it maintains the excellent property that the original cover can be losslessly recovered after embedded data is extracted while protecting the image content's confidentiality. All previous methods embed data by reversibly vacating room from the encrypted images, which may be subject to some errors on data extraction and/or image restoration. In this paper, we propose a novel method by reserving room before encryption with a traditional RDH algorithm, and thus it is easy for the data hider to reversibly embed data in the encrypted image. The proposed method can achieve real reversibility, that is, data extraction and image recovery are free of any error. Experiments show that this novel method can embed more than 10 times as large payloads for the same image quality as the previous methods, such as for PSNR=40 dB.

Objective Quality Assessment of Tone-Mapped Images

Abstract: Tone-mapping operators (TMOs) that convert high dynamic range (HDR) to low dynamic range (LDR) images provide practically useful tools for the visualization of HDR images on standard LDR displays. Different TMOs create different tone-mapped images, and a natural question is which one has the best quality. Without an appropriate quality measure, different TMOs cannot be compared, and further improvement is directionless. Subjective rating may be a reliable evaluation method, but it is expensive and time consuming, and more importantly, is difficult to be embedded into optimization frameworks. Here we propose an objective quality assessment algorithm for tone-mapped images by combining: 1) a multiscale signal fidelity measure on the basis of a modified structural similarity index and 2) a naturalness measure on the basis of intensity statistics of natural images. Validations using independent subject-rated image databases show good correlations between subjective ranking score and the proposed tone-mapped image quality index (TMQI). Furthermore, we demonstrate the extended applications of TMQI using two examples - parameter tuning for TMOs and adaptive fusion of multiple tone-mapped images.

Learning without Human Scores for Blind Image Quality Assessment

Abstract: General purpose blind image quality assessment (BIQA) has been recently attracting significant attention in the fields of image processing, vision and machine learning. State-of-the-art BIQA methods usually learn to evaluate the image quality by regression from human subjective scores of the training samples. However, these methods need a large number of human scored images for training, and lack an explicit explanation of how the image quality is affected by image local features. An interesting question is then: can we learn for effective BIQA without using human scored images? This paper makes a good effort to answer this question. We partition the distorted images into overlapped patches, and use a percentile pooling strategy to estimate the local quality of each patch. Then a quality-aware clustering (QAC) method is proposed to learn a set of centroids on each quality level. These centroids are then used as a codebook to infer the quality of each patch in a given image, and subsequently a perceptual quality score of the whole image can be obtained. The proposed QAC based BIQA method is simple yet effective. It not only has comparable accuracy to those methods using human scored images in learning, but also has merits such as high linearity to human perception of image quality, real-time implementation and availability of image local quality map.

Fast Image Super-Resolution Based on In-Place Example Regression

Abstract: We propose a fast regression model for practical single image super-resolution based on in-place examples, by leveraging two fundamental super-resolution approaches- learning from an external database and learning from self-examples. Our in-place self-similarity refines the recently proposed local self-similarity by proving that a patch in the upper scale image have good matches around its origin location in the lower scale image. Based on the in-place examples, a first-order approximation of the nonlinear mapping function from low-to high-resolution image patches is learned. Extensive experiments on benchmark and real-world images demonstrate that our algorithm can produce natural-looking results with sharp edges and preserved fine details, while the current state-of-the-art algorithms are prone to visual artifacts. Furthermore, our model can easily extend to deal with noise by combining the regression results on multiple in-place examples for robust estimation. The algorithm runs fast and is particularly useful for practical applications, where the input images typically contain diverse textures and they are potentially contaminated by noise or compression artifacts.

Full-reference quality assessment of stereopairs accounting for rivalry

Abstract: We develop a framework for assessing the quality of stereoscopic images that have been afflicted by possibly asymmetric distortions. An intermediate image is generated which when viewed stereoscopically is designed to have a perceived quality close to that of the cyclopean image. We hypothesize that performing stereoscopic QA on the intermediate image yields higher correlations with human subjective judgments. The experimental results confirm the hypothesis and show that the proposed framework significantly outperforms conventional 2D QA metrics when predicting the quality of stereoscopically viewed images that may have been asymmetrically distorted.

A limited-angle CT reconstruction method based on anisotropic TV minimization.

Abstract: This paper presents a compressed sensing (CS)-inspired reconstruction method for limited-angle computed tomography (CT). Currently, CS-inspired CT reconstructions are often performed by minimizing the total variation (TV) of a CT image subject to data consistency. A key to obtaining high image quality is to optimize the balance between TV-based smoothing and data fidelity. In the case of the limited-angle CT problem, the strength of data consistency is angularly varying. For example, given a parallel beam of x-rays, information extracted in the Fourier domain is mostly orthogonal to the direction of x-rays, while little is probed otherwise. However, the TV minimization process is isotropic, suggesting that it is unfit for limited-angle CT. Here we introduce an anisotropic TV minimization method to address this challenge. The advantage of our approach is demonstrated in numerical simulation with both phantom and real CT images, relative to the TV-based reconstruction.

Translation position determination in ptychographic coherent diffraction imaging

Abstract: Accurate knowledge of translation positions is essential in ptychography to achieve a good image quality and the diffraction limited resolution. We propose a method to retrieve and correct position errors during the image reconstruction iterations. Sub-pixel position accuracy after refinement is shown to be achievable within several tens of iterations. Simulation and experimental results for both optical and X-ray wavelengths are given. The method improves both the quality of the retrieved object image and relaxes the position accuracy requirement while acquiring the diffraction patterns.

Image Sharpness Assessment Based on Local Phase Coherence

Abstract: Sharpness is an important determinant in visual assessment of image quality. The human visual system is able to effortlessly detect blur and evaluate sharpness of visual images, but the underlying mechanism is not fully understood. Existing blur/sharpness evaluation algorithms are mostly based on edge width, local gradient, or energy reduction of global/local high frequency content. Here we understand the subject from a different perspective, where sharpness is identified as strong local phase coherence (LPC) near distinctive image features evaluated in the complex wavelet transform domain. Previous LPC computation is restricted to be applied to complex coefficients spread in three consecutive dyadic scales in the scale-space. Here we propose a flexible framework that allows for LPC computation in arbitrary fractional scales. We then develop a new sharpness assessment algorithm without referencing the original image. We use four subject-rated publicly available image databases to test the proposed algorithm, which demonstrates competitive performance when compared with state-of-the-art algorithms.

Iterative reconstruction techniques for computed tomography part 2: initial results in dose reduction and image quality

Abstract: Objectives To present the results of a systematic literature search aimed at determining to what extent the radiation dose can be reduced with iterative reconstruction (IR) for cardiopulmonary and body imaging with computed tomography (CT) in the clinical setting and what the effects on image quality are with IR versus filtered back-projection (FBP) and to provide recommendations for future research on IR.

No-Reference Quality Assessment of Natural Stereopairs

Abstract: We develop a no-reference binocular image quality assessment model that operates on static stereoscopic images. The model deploys 2D and 3D features extracted from stereopairs to assess the perceptual quality they present when viewed stereoscopically. Both symmetric- and asymmetric-distorted stereopairs are handled by accounting for binocular rivalry using a classic linear rivalry model. The NSS features are used to train a support vector machine model to predict the quality of a tested stereopair. The model is tested on the LIVE 3D Image Quality Database, which includes both symmetric- and asymmetric-distorted stereoscopic 3D images. The experimental results show that our proposed model significantly outperforms the conventional 2D full-reference QA algorithms applied to stereopairs, as well as the 3D full-reference IQA algorithms on asymmetrically distorted stereopairs.

PiCam: an ultra-thin high performance monolithic camera array

Abstract: We present PiCam (Pelican Imaging Camera-Array), an ultra-thin high performance monolithic camera array, that captures light fields and synthesizes high resolution images along with a range image (scene depth) through integrated parallax detection and superresolution. The camera is passive, supporting both stills and video, low light capable, and small enough to be included in the next generation of mobile devices including smartphones. Prior works [Rander et al. 1997; Yang et al. 2002; Zhang and Chen 2004; Tanida et al. 2001; Tanida et al. 2003; Duparre et al. 2004] in camera arrays have explored multiple facets of light field capture - from viewpoint synthesis, synthetic refocus, computing range images, high speed video, and micro-optical aspects of system miniaturization. However, none of these have addressed the modifications needed to achieve the strict form factor and image quality required to make array cameras practical for mobile devices. In our approach, we customize many aspects of the camera array including lenses, pixels, sensors, and software algorithms to achieve imaging performance and form factor comparable to existing mobile phone cameras.Our contributions to the post-processing of images from camera arrays include a cost function for parallax detection that integrates across multiple color channels, and a regularized image restoration (superresolution) process that takes into account all the system degradations and adapts to a range of practical imaging conditions. The registration uncertainty from the parallax detection process is integrated into a Maximum-a-Posteriori formulation that synthesizes an estimate of the high resolution image and scene depth. We conclude with some examples of our array capabilities such as postcapture (still) refocus, video refocus, view synthesis to demonstrate motion parallax, 3D range images, and briefly address future work.

Perceptual Quality Metric With Internal Generative Mechanism

Abstract: Objective image quality assessment (IQA) aims to evaluate image quality consistently with human perception Most of the existing perceptual IQA metrics cannot accurately represent the degradations from different types of distortion, eg, existing structural similarity metrics perform well on content-dependent distortions while not as well as peak signal-to-noise ratio (PSNR) on content-independent distortions In this paper, we integrate the merits of the existing IQA metrics with the guide of the recently revealed internal generative mechanism (IGM) The IGM indicates that the human visual system actively predicts sensory information and tries to avoid residual uncertainty for image perception and understanding Inspired by the IGM theory, we adopt an autoregressive prediction algorithm to decompose an input scene into two portions, the predicted portion with the predicted visual content and the disorderly portion with the residual content Distortions on the predicted portion degrade the primary visual information, and structural similarity procedures are employed to measure its degradation; distortions on the disorderly portion mainly change the uncertain information and the PNSR is employed for it Finally, according to the noise energy deployment on the two portions, we combine the two evaluation results to acquire the overall quality score Experimental results on six publicly available databases demonstrate that the proposed metric is comparable with the state-of-the-art quality metrics

Filtered Back Projection, Adaptive Statistical Iterative Reconstruction, and a Model-based Iterative Reconstruction in Abdominal CT: An Experimental Clinical Study

Abstract: Our experimental data suggest that the use of model-based iterative reconstruction considerably improved image quality compared with that of both the adaptive statistical iterative reconstruction algorithm and the noniterative filtered back projection.

Fast full-color computational imaging with single-pixel detectors

Abstract: Single-pixel detectors can be used as imaging devices by making use of structured illumination These systems work by correlating a changing incident light field with signals measured on a photodiode to derive an image of an object In this work we demonstrate a system that utilizes a digital light projector to illuminate a scene with approximately 1300 different light patterns every second and correlate these with the back scattered light measured by three spectrally-filtered single-pixel photodetectors to produce a full-color high-quality image in a few seconds of data acquisition We utilize a differential light projection method to self normalize the measured signals, improving the reconstruction quality whilst making the system robust to external sources of noise This technique can readily be extended for imaging applications at non-visible wavebands

Exposing Digital Image Forgeries by Illumination Color Classification

Abstract: For decades, photographs have been used to document space-time events and they have often served as evidence in courts. Although photographers are able to create composites of analog pictures, this process is very time consuming and requires expert knowledge. Today, however, powerful digital image editing software makes image modifications straightforward. This undermines our trust in photographs and, in particular, questions pictures as evidence for real-world events. In this paper, we analyze one of the most common forms of photographic manipulation, known as image composition or splicing. We propose a forgery detection method that exploits subtle inconsistencies in the color of the illumination of images. Our approach is machine-learning-based and requires minimal user interaction. The technique is applicable to images containing two or more people and requires no expert interaction for the tampering decision. To achieve this, we incorporate information from physics- and statistical-based illuminant estimators on image regions of similar material. From these illuminant estimates, we extract texture- and edge-based features which are then provided to a machine-learning approach for automatic decision-making. The classification performance using an SVM meta-fusion classifier is promising. It yields detection rates of 86% on a new benchmark dataset consisting of 200 images, and 83% on 50 images that were collected from the Internet.

Free-Breathing Contrast-Enhanced Multiphase MRI of the Liver Using a Combination of Compressed Sensing, Parallel Imaging, and Golden-Angle Radial Sampling

Abstract: Assessment of arterial and venous phases of enhancement is essential for liver lesion detection and characterization.1,2 Contrast-enhanced multiphase liver magnetic resonance (MR) examination is usually performed using a T1-weighted fat-saturated 3-dimensional (3D) volumetric interpolated sequence with cartesian k-space sampling in a breath hold (BH). However, this method is sensitive to respiratory motion and can result in suboptimal images in patients who cannot adequately hold their breath. Although parallel-imaging and partial-Fourier techniques are usually used for accelerating the examination, this may be insufficient in elderly patients, patients with debilitations, or pediatric patients who have severely limited breath-holding capacity.3,4 Furthermore, achievable in-plane spatial resolution and anatomic coverage remain limited because of the need to acquire data within a BH. Recently, a more motion-robust 3D gradient-echo sequence has been developed (radial VIBE) that uses the “stack-of-stars”scheme to acquire volumetric k-space data, where radial sampling is performed in-plane (along ky and kx) and cartesian sampling is used along the slice dimension (kz).5,6 Studies have shown that free-breathing acquisitions with the stack-of-stars radial VIBE sequence can yield images of comparable image quality with conventional BH examination at the expense of a longer acquisition time.7,8 This relatively long acquisition time limits its utility for dynamic liver imaging, which requires multiphase acquisitions with temporal resolution of 15 to 20 seconds. One potential solution to improve the temporal resolution is the application of the compressed sensing (CS) concept, which has recently emerged as a powerful tool for fast imaging by exploiting redundancies in the images.9 Successful application of CS requires sparsity, incoherence, and nonlinear reconstruction. Magnetic resonance images often can be represented using only few coefficients in an appropriate transform basis. Multiphase liver MRI is a perfect candidate for CS because of extensive spatiotemporal data correlations that result in sparse representations. Compressed sensing can be synergistically combined with parallel imaging to further increase imaging speed.10–12 High level of incoherence can be achieved by using irregular k-space sampling patterns. Radial sampling of k-space compares favorably with conventional cartesian schemes for CS because of the inherent presence of incoherent aliasing artifacts from undersampled radial trajectories,10 which are essential for application of the CS reconstruction. We have recently developed a reconstruction technique that combines CS with parallel imaging for radially acquired dynamic MRI.13 Two different types of radial acquisition schemes are investigated in this study: the interleaved angle-bisection scheme and the golden-angle scheme, which mainly differ in the temporal order of the k-space sampling. With the interleaved angle-bisection scheme, radial spokes are acquired at a regular angular distance (Fig. 1) in multiple interleaves, such that all spokes from 1 interleave intersect the spokes from the previously acquired interleave.14 With the recently proposed golden-angle acquisition scheme, on the other hand, the angle of the acquired spokes is continuously increased by 111.25 degrees during the acquisition, resulting in a series of complementary radial spokes with large angular distance that, for an arbitrary number of spokes, always add up to an approximately uniform angular coverage of the k-space15 (Fig. 1). FIGURE 1 Schematic of sampling scheme for interleaved angle-bisection and continuous golden-angle acquisitions. Temporal frames in the bisection scheme need to be predefined, whereas the golden-angle scheme provides freedom in defining temporal frames retrospectively ... The purposes of this study were to demonstrate the feasibility of performing free-breathing multiphase liver MRI using a combination of CS and parallel imaging with golden-angle (golden-angle radial sparse parallel [GRASP]) and interleaved-angle (interleaved-angle radial sparse parallel [IARASP]) radial sampling scheme and to compare image quality of GRASP and IARASP with conventional BH T1-weighted gradient-echo imaging with cartesian sampling (volumetric interpolate breath hold examination [BH-VIBE]) in healthy participants with normal breath-holding capacity.

Estimation of uncertainty bounds for individual particle image velocimetry measurements from cross-correlation peak ratio

Abstract: Numerous studies have established firmly that particle image velocimetry (PIV) is a robust method for non-invasive, quantitative measurements of fluid velocity, and that when carefully conducted, typical measurements can accurately detect displacements in digital images with a resolution well below a single pixel (in some cases well below a hundredth of a pixel). However, to date, these estimates have only been able to provide guidance on the expected error for an average measurement under specific image quality and flow conditions. This paper demonstrates a new method for estimating the uncertainty bounds to within a given confidence interval for a specific, individual measurement. Here, cross-correlation peak ratio, the ratio of primary to secondary peak height, is shown to correlate strongly with the range of observed error values for a given measurement, regardless of flow condition or image quality. This relationship is significantly stronger for phase-only generalized cross-correlation PIV processing, while the standard correlation approach showed weaker performance. Using an analytical model of the relationship derived from synthetic data sets, the uncertainty bounds at a 95% confidence interval are then computed for several artificial and experimental flow fields, and the resulting errors are shown to match closely to the predicted uncertainties. While this method stops short of being able to predict the true error for a given measurement, knowledge of the uncertainty level for a PIV experiment should provide great benefits when applying the results of PIV analysis to engineering design studies and computational fluid dynamics validation efforts. Moreover, this approach is exceptionally simple to implement and requires negligible additional computational cost.

Highly accelerated real-time cardiac cine MRI using k–t SPARSE-SENSE

Abstract: For patients with impaired breath-hold capacity and/or arrhythmias, real-time cine MRI may be more clinically useful than breath-hold cine MRI. However, commercially available real-time cine MRI methods using parallel imaging typically yield relatively poor spatio-temporal resolution due to their low image acquisition speed. We sought to achieve relatively high spatial resolution (∼2.5 × 2.5 mm(2)) and temporal resolution (∼40 ms), to produce high-quality real-time cine MR images that could be applied clinically for wall motion assessment and measurement of left ventricular function. In this work, we present an eightfold accelerated real-time cardiac cine MRI pulse sequence using a combination of compressed sensing and parallel imaging (k-t SPARSE-SENSE). Compared with reference, breath-hold cine MRI, our eightfold accelerated real-time cine MRI produced significantly worse qualitative grades (1-5 scale), but its image quality and temporal fidelity scores were above 3.0 (adequate) and artifacts and noise scores were below 3.0 (moderate), suggesting that acceptable diagnostic image quality can be achieved. Additionally, both eightfold accelerated real-time cine and breath-hold cine MRI yielded comparable left ventricular function measurements, with coefficient of variation <10% for left ventricular volumes. Our proposed eightfold accelerated real-time cine MRI with k-t SPARSE-SENSE is a promising modality for rapid imaging of myocardial function.

Reversible Watermarking Based on Invariant Image Classification and Dynamic Histogram Shifting

Abstract: In this paper, we propose a new reversible watermarking scheme. One first contribution is a histogram shifting modulation which adaptively takes care of the local specificities of the image content. By applying it to the image prediction-errors and by considering their immediate neighborhood, the scheme we propose inserts data in textured areas where other methods fail to do so. Furthermore, our scheme makes use of a classification process for identifying parts of the image that can be watermarked with the most suited reversible modulation. This classification is based on a reference image derived from the image itself, a prediction of it, which has the property of being invariant to the watermark insertion. In that way, the watermark embedder and extractor remain synchronized for message extraction and image reconstruction. The experiments conducted so far, on some natural images and on medical images from different modalities, show that for capacities smaller than 0.4 bpp, our method can insert more data with lower distortion than any existing schemes. For the same capacity, we achieve a peak signal-to-noise ratio (PSNR) of about 1-2 dB greater than with the scheme of Hwang , the most efficient approach actually.

Automatic Prediction of Perceptual Image and Video Quality

Abstract: Finding ways to monitor and control the perceptual quality of digital visual media has become a pressing concern as the volume being transported and viewed continues to increase exponentially. This paper discusses the principles and methods of modern algorithms for automatically predicting the quality of visual signals. By casting the problem as analogous to assessing the efficacy of a visual communication system, it is possible to divide the quality assessment problem into understandable modeling subproblems. Along the way, we will visit models of natural images and videos, of visual perception, and a broad spectrum of applications.

CMOS Image Sensor With Per-Column ΣΔ ADC and Programmable Compressed Sensing

Abstract: A CMOS image sensor architecture with built-in single-shot compressed sensing is described. The image sensor employs a conventional 4-T pixel and per-column ΣΔ ADCs. The compressed sensing measurements are obtained via a column multiplexer that sequentially applies randomly selected pixel values to the input of each ΣΔ modulator. At the end of readout, each ADC outputs a quantized value of the average of the pixel values applied to its input. The image is recovered from the random linear measurements off-chip using numerical optimization algorithms. To demonstrate this architecture, a 256x256 pixel CMOS image sensor is fabricated in 0.15 μm CIS process. The sensor can operate in compressed sensing mode with compression ratio 1/4, 1/8, or 1/16 at 480, 960, or 1920 fps, respectively, or in normal capture mode with no compressed sensing at a maximum frame rate of 120 fps. Measurement results demonstrate capture in compressed sensing mode at roughly the same readout noise of 351 μVrms and power consumption of 96.2 mW of normal capture at 120 fps. This performance is achieved with only 1.8% die area overhead. Image reconstruction shows modest quality loss relative to normal capture and significantly higher image quality than downsampling.

Multi-MHz retinal OCT

Abstract: We analyze the benefits and problems of in vivo optical coherence tomography (OCT) imaging of the human retina at A-scan rates in excess of 1 MHz, using a 1050 nm Fourier-domain mode-locked (FDML) laser. Different scanning strategies enabled by MHz OCT line rates are investigated, and a simple multi-volume data processing approach is presented. In-vivo OCT of the human ocular fundus is performed at different axial scan rates of up to 6.7 MHz. High quality non-mydriatic retinal imaging over an ultra-wide field is achieved by a combination of several key improvements compared to previous setups. For the FDML laser, long coherence lengths and 72 nm wavelength tuning range are achieved using a chirped fiber Bragg grating in a laser cavity at 419.1 kHz fundamental tuning rate. Very large data sets can be acquired with sustained data transfer from the data acquisition card to host computer memory, enabling high-quality averaging of many frames and of multiple aligned data sets. Three imaging modes are investigated: Alignment and averaging of 24 data sets at 1.68 MHz axial line rate, ultra-dense transverse sampling at 3.35 MHz line rate, and dual-beam imaging with two laser spots on the retina at an effective line rate of 6.7 MHz.

Virtual Monochromatic Spectral Imaging with Fast Kilovoltage Switching: Reduction of Metal Artifacts at CT

Abstract: The ability to obtain virtual monochromatic spectral images with dual-energy CT gives this technique potential advantages over conventional CT in terms of reduced metal artifacts, improved image quality, and greater diagnostic value.

Image Quality Assessment Using Multi-Method Fusion

Abstract: A new methodology for objective image quality assessment (IQA) with multi-method fusion (MMF) is presented in this paper. The research is motivated by the observation that there is no single method that can give the best performance in all situations. To achieve MMF, we adopt a regression approach. The new MMF score is set to be the nonlinear combination of scores from multiple methods with suitable weights obtained by a training process. In order to improve the regression results further, we divide distorted images into three to five groups based on the distortion types and perform regression within each group, which is called “context-dependent MMF” (CD-MMF). One task in CD-MMF is to determine the context automatically, which is achieved by a machine learning approach. To further reduce the complexity of MMF, we perform algorithms to select a small subset from the candidate method set. The result is very good even if only three quality assessment methods are included in the fusion process. The proposed MMF method using support vector regression is shown to outperform a large number of existing IQA methods by a significant margin when being tested in six representative databases.

Accurate Blur Models vs. Image Priors in Single Image Super-resolution

Abstract: Over the past decade, single image Super-Resolution (SR) research has focused on developing sophisticated image priors, leading to significant advances. Estimating and incorporating the blur model, that relates the high-res and low-res images, has received much less attention, however. In particular, the reconstruction constraint, namely that the blurred and down sampled high-res output should approximately equal the low-res input image, has been either ignored or applied with default fixed blur models. In this work, we examine the relative importance of the image prior and the reconstruction constraint. First, we show that an accurate reconstruction constraint combined with a simple gradient regularization achieves SR results almost as good as those of state-of-the-art algorithms with sophisticated image priors. Second, we study both empirically and theoretically the sensitivity of SR algorithms to the blur model assumed in the reconstruction constraint. We find that an accurate blur model is more important than a sophisticated image prior. Finally, using real camera data, we demonstrate that the default blur models of various SR algorithms may differ from the camera blur, typically leading to over-smoothed results. Our findings highlight the importance of accurately estimating camera blur in reconstructing raw lowers images acquired by an actual camera.

No reference color image contrast and quality measures

Abstract: No-reference (NR) image quality assessment is essential in evaluating the performance of image enhancement and retrieval algorithms. Much effort has been made in recent years to develop objective NR grayscale and color image quality metrics that correlate with perceived quality evaluations. Unfortunately, only limited success has been achieved and most existing NR quality assessment is feasible only when prior knowledge about the types of image distortion is available. This paper present: a) a new NR contrast based grayscale image contrast measure: Root Mean Enhancement (RME); b) a NR color RME contrast measure CRME which explores the three dimensional contrast relationships of the RGB color channels; c) a NR color quality measure Color Quality Enhancement (CQE) which is based on the linear combination of colorfulness, sharpness and contrast. Computer simulations demonstrate that each measure has its own advantages: the CRME measure is fast and suitable for real time processing of low contrast images; the CQE measure can be used for a wider variety of distorted images. The effectiveness of the presented measures is demonstrated by using the TID2008 database. Experimental results also show strong correlations between the presented measures and Mean Opinion Score (MOS)1.