Competitive Image Quality Research Articles

A Fast View Synthesis Implementation Method for Light Field Applications

View synthesis (VS) for light field images is a very time-consuming task due to the great quantity of involved pixels and intensive computations, which may prevent it from the practical three-dimensional real-time systems. In this article, we propose an acceleration approach for deep learning-based light field view synthesis, which can significantly reduce calculations by using compact-resolution (CR) representation and super-resolution (SR) techniques, as well as light-weight neural networks. The proposed architecture has three cascaded neural networks, including a CR network to generate the compact representation for original input views, a VS network to synthesize new views from down-scaled compact views, and a SR network to reconstruct high-quality views with full resolution. All these networks are jointly trained with the integrated losses of CR, VS, and SR networks. Moreover, due to the redundancy of deep neural networks, we use the efficient light-weight strategy to prune filters for simplification and inference acceleration. Experimental results demonstrate that the proposed method can greatly reduce the processing time and become much more computationally efficient with competitive image quality.

Video Speed Switching of Plasmonic Structural Colors with High Contrast and Superior Lifetime.

Reflective displays or "electronic paper" technologies provide a solution to the high energy consumption of emissive displays by simply utilizing ambient light. However, it has proven challenging to develop electronic paper with competitive image quality and video speed capabilities. Here, the first technology that provides video speed switching of structural colors with high contrast over the whole visible is shown. Importantly, this is achieved with abroadband-absorbing polarization-insensitive electrochromic polymer instead of liquid crystals, which makes it possible to maintain high reflectivity. It is shown that promoting electrophoretic ion transport (drift motion) improves the switch speed. In combination with new nanostructures that have high surface curvature, this enables video speed switching (20ms) at high contrast (50% reflectivity change). A detailed analysis of the optical signal during switching shows that the polaron formation starts to obey first order reaction kinetics in the video speed regime. Additionally, the system still operates at ultralow power consumption during video speed switching (<1mW cm-2 ) and has negligible power consumption (<1 µW cm-2 ) in bistability mode. Finally, the fast switching increases device lifetime to at least 107 cycles, an order of magnitude more than state-of-the-art.

Competitive Improvement of the Time Complexity to Encode Fractal Image: By Applying Symmetric Central Pixel of the Block

By combining the basics of self-similarity, scaling correlation, and statistical components, Benoit Mandelbrot formulated the idea of a natural fractal entity, an entity described by those fundamentals. As a result of these principles, fractal image codings are being used in many substantial applications already, such as image compression, image signature, image watermarking, image attribute extraction, and even image texture segmentation. Thus, while fractal image coding is relatively new in the field of image encoding, it has gained broad acceptance at a rapid pace. In light of its beneficial qualities, such as quick decomposition, high compression ratio, and the independence of resolution at any size make these applications conceivable. However, compared to its advantages, fractal image coding is extremely time-complex and so remarkably expensive, which hinders its prevalence. A wide hunting domain blocks for the relevant range blocks caused this difficulty. We proposed several improvements to the Jacquin design in this paper. We first used max-pooling as an alternative for the medium bonding of spatial contractions to validate the value of the edge textures of the block. Secondly, we construct the odd-size pixel block alternative to an even-size pixel block for validation of the symmetric central pixel (CP). Finally, before the search started, we proposed a shortening of block space, using the central pixel of the block to convert each eight-bit pixel to a two-bit pixel. As a consequence, the symmetrical CP of odd pixels block, reduction of block space, and edge pixel selection accomplished faster coding and competitive image quality than existing known exhaustive search algorithms.

Detector clothes for MRI: A wearable array receiver based on liquid metal in elastic tubes

In modern magnetic resonance imaging, signal detection is performed by dense arrays of radiofrequency resonators. Tight-fitting arrays boost the sensitivity and speed of imaging. However, current devices are rigid and cage-like at the expense of patient comfort. They also constrain posture, limiting the examination of joints. For better ergonomics and versatility, detectors should be flexible, adapt to individual anatomy, and follow posture. Towards this goal, the present work proposes a novel design based on resonators formed by liquid metal in polymer tubes. Textile integration creates lightweight, elastic devices that are worn like pieces of clothing. A liquid-metal array tailored to the human knee is shown to deliver competitive image quality while self-adapting to individual anatomy and adding the ability to image flexion of the joint. Relative to other options for stretchable conductors, liquid metal in elastic tubes stands out by reconciling excellent electrical and mechanical properties with ease of manufacturing.

Offset Aperture: A Passive Single-Lens Camera for Depth Sensing

Numerous camera sensors have been recently proposed to estimate the depth of objects in the scene. However, many of these sensors either require multiple imaging elements or an active illuminant. In this paper, we propose a new depth camera, termed offset aperture (OA) camera, which provides depth of the scene with a single shot without an active illuminant. The camera is based on a 4-color sensor that senses the infrared (IR) signal along with the visible RGB signals. Separate OAs are used for IR and RGB images. A depth-dependent disparity is generated between the IR and RGB images. There is no disparity across the R, G, and B channel images, which allows the RGB image to be well-aligned. Numerous techniques are used in this work to observe good-quality depth maps and images. Leakage compensation has been proposed to remove crosstalk between the IR and RGB images. A comparison with another 4-color sensor called Dual Aperture camera shows that OA camera provides 88% improvement in depth sensitivity and 45% improvement in color quality, on average. The proposed OA camera can be used for numerous applications including occlusion handling in stereo, selective focus, and night vision. The advantages of the proposed OA camera include competitive image quality, capability of depth extraction, well-aligned RGB images, and small footprint of the camera, which make OA camera a suitable candidate for mobile applications.

Rapid anatomical brain imaging using spiral acquisition and an expanded signal model

We report the deployment of spiral acquisition for high-resolution structural imaging at 7T. Long spiral readouts are rendered manageable by an expanded signal model including static off-resonance and B0 dynamics along with k-space trajectories and coil sensitivity maps. Image reconstruction is accomplished by inversion of the signal model using an extension of the iterative non-Cartesian SENSE algorithm. Spiral readouts up to 25 ms are shown to permit whole-brain 2D imaging at 0.5 mm in-plane resolution in less than a minute. A range of options is explored, including proton-density and T2* contrast, acceleration by parallel imaging, different readout orientations, and the extraction of phase images. Results are shown to exhibit competitive image quality along with high geometric consistency.

Super-resolution intracranial quiescent interval slice-selective magnetic resonance angiography.

To evaluate the combination of nonenhanced quiescent-interval slice-selective (QISS) magnetic resonance angiography (MRA) with super-resolution reconstruction for portraying the intracranial arteries. The intracranial arteries of seven volunteers were imaged at 3T using QISS MRA acquired with a flow-compensated fast low-angle shot (FLASH) readout and thin overlapping slices. The impacts of super-resolution reconstruction and various acquisition parameters on the delineation of intracranial arteries were quantified using four metrics: arterial-to-background contrast-to-noise ratio (CNR), arterial-to-background contrast, arterial sharpness, and arterial full-width-at-half-maximum (FWHM). Three-dimensional time-of-flight (TOF) MRA was also acquired. For similar voxel sizes, QISS MRA displayed the intracranial arteries with an arterial-to-background contrast that exceeded 3D TOF MRA by 59-84%, depending on the k-space sampling trajectory (P < 0.001). Super-resolution reconstruction improved CNR, contrast, and sharpness, while reducing arterial FWHM (P < 0.001). Cardiac triggering provided minimal benefits, while Cartesian sampling provided higher CNR than radial sampling for multishot QISS (P < 0.05). Scan time for a complete intracranial MRA was <90 s using an ungated single-shot QISS acquisition. Thin, overlapping-slice QISS leveraging super-resolution reconstruction is a flexible approach for intracranial MRA that provides competitive image quality to standard-of-care 3D TOF, with the potential for reduced sensitivity to in-plane flow saturation and motion artifacts. Magn Reson Med 79:683-691, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Dynamic Stochastic Resonance Based Diffusion-Weighted Magnetic Resonance Image Enhancement Using Multi-Objective Particle Swarm Optimization

Diffusion weighted (DW) magnetic resonance (MR) imaging maps the diffusion process of water in the tissues. DW-MR image is useful to probe the tissue microstructure, but suffers from inherent low signal to noise ratio and poor contrast. Dynamic stochastic resonance (DSR) utilizes the noise to enhance the low contrast image where the quality of the processed image depends on the bistability parameters of DSR and the number of iterations. This paper presents an approach that optimally finds the bistability parameters and number of iterations for the maximization of competitive image quality indices: contrast enhancement factor and mean opinion score using multi-objective particle swarm optimization. The proposed Particle Swarm Optimization optimized DSR algorithm has been tested on 40 DW-MR brain images of different subjects. The quantified results show average contrast enhancement factor, 1.603 and average perceptual quality measure, 9.508. These values are significantly higher than image quality indices of original image, the images that are produced by conventional enhancement methods and filtering followed by enhancement methods.

Blind image quality assessment by relative gradient statistics and adaboosting neural network

The image gradient is a commonly computed image feature and a potentially predictive factor for image quality assessment (IQA). Indeed, it has been successfully used for both full- and no- reference image quality prediction. However, the gradient orientation has not been deeply explored as a predictive source of information for image quality assessment. Here we seek to amend this by studying the quality relevance of the relative gradient orientation, viz., the gradient orientation relative to the surround. We also deploy a relative gradient magnitude feature which accounts for perceptual masking and utilize an AdaBoosting back-propagation (BP) neural network to map the image features to image quality. The generalization of the AdaBoosting BP neural network results in an effective and robust quality prediction model. The new model, called Oriented Gradients Image Quality Assessment (OG-IQA), is shown to deliver highly competitive image quality prediction performance as compared with the most popular IQA approaches. Furthermore, we show that OG-IQA has good database independence properties and a low complexity.

HYPERSPECTRAL IMAGE DENOISING WITH CUBIC TOTAL VARIATION MODEL

Abstract. Image noise is generated unavoidably in the hyperspectral image acquision process and has a negative effect on subsequent image analysis. Therefore, it is necessary to perform image denoising for hyperspectral images. This paper proposes a cubic total variation (CTV) model by combining the 2-D total variation model for spatial domain with the 1-D total variation model for spectral domain, and then applies the termed CTV model to hyperspectral image denoising. The augmented Lagrangian method is utilized to improve the speed of solution of the desired hyperspectral image. The experimental results suggest that the proposed method can achieve competitive image quality.

P‐93: High Performance 17.0″ SVGA OCB Panel with Fast Initial Bend Transition

AbstractRecently, the vigorous researches on the moving picture quality of TFT‐LCDs have been made to overcome their drawbacks in replacing CRTs for TV application. Among them, the fast response OCB mode is thought to have the highest potential for TV application, even though its image quality still needs to be improved. In this regard, we have optimized the cell parameters of OCB mode to get wide viewing angle, high contrast ratio and transmittance as well as the fast response time. Based on these parameters, we have improved image qualities remarkably by adopting these to 17.0″ SVGA proto‐panel, which shows the contrast ratio over 250, viewing angle over 80 degree for all direction except rubbing direction, response time under 7 ms including inter‐gray scale and the transmittance of 90% of TN mode. Especially, by adopting new driving scheme, we considerably reduced the initial bend transition time, which is known as one of drawbacks in OCB mode. We are continuing further research to get the competitive image quality and good reliability, which is comparable to commercially used PVA or IPS mode.

Fast lattice-based gain-shape vector quantisation for image-sequence coding

A fast lattice-based gain/shape vector-quantisation method (FLGSVQ) is presented. The proposed technique combines the basic ideas of gain/shape vector quantisation (GSVQ) with lattice codes to offer an encoding process which is extremely fast compared with the conventional GSVQ. The main aim is to show how vector quantisers designed using the LBG algorithm can be substituted by lattice-based ones with vast reductions in encoding complexity, at a competitive image quality. A simple single-/two-stage coding system based on lattice quantisation is tested for interframe predictive coding of image sequences. Simulation results are provided to compare the performance of the proposed method with the conventional LBG-designed vector quantisation. These results, along with subjective evaluations, clearly demonstrate that lattice quantisation can be competitive with vector quantisation in terms of signal-to-noise performance, only at a very small fraction (4%) of the vector-quantiser complexity.