1,044 research outputs found
Digital Color Imaging
This paper surveys current technology and research in the area of digital
color imaging. In order to establish the background and lay down terminology,
fundamental concepts of color perception and measurement are first presented
us-ing vector-space notation and terminology. Present-day color recording and
reproduction systems are reviewed along with the common mathematical models
used for representing these devices. Algorithms for processing color images for
display and communication are surveyed, and a forecast of research trends is
attempted. An extensive bibliography is provided
Color image quality measures and retrieval
The focus of this dissertation is mainly on color image, especially on the images with lossy compression. Issues related to color quantization, color correction, color image retrieval and color image quality evaluation are addressed. A no-reference color image quality index is proposed. A novel color correction method applied to low bit-rate JPEG image is developed. A novel method for content-based image retrieval based upon combined feature vectors of shape, texture, and color similarities has been suggested. In addition, an image specific color reduction method has been introduced, which allows a 24-bit JPEG image to be shown in the 8-bit color monitor with 256-color display. The reduction in download and decode time mainly comes from the smart encoder incorporating with the proposed color reduction method after color space conversion stage. To summarize, the methods that have been developed can be divided into two categories: one is visual representation, and the other is image quality measure.
Three algorithms are designed for visual representation:
(1) An image-based visual representation for color correction on low bit-rate JPEG images. Previous studies on color correction are mainly on color image calibration among devices. Little attention was paid to the compressed image whose color distortion is evident in low bit-rate JPEG images. In this dissertation, a lookup table algorithm is designed based on the loss of PSNR in different compression ratio.
(2) A feature-based representation for content-based image retrieval. It is a concatenated vector of color, shape, and texture features from region of interest (ROI).
(3) An image-specific 256 colors (8 bits) reproduction for color reduction from 16 millions colors (24 bits). By inserting the proposed color reduction method into a JPEG encoder, the image size could be further reduced and the transmission time is also reduced. This smart encoder enables its decoder using less time in decoding.
Three algorithms are designed for image quality measure (IQM):
(1) A referenced IQM based upon image representation in very low-dimension. Previous studies on IQMs are based on high-dimensional domain including spatial and frequency domains. In this dissertation, a low-dimensional domain IQM based on random projection is designed, with preservation of the IQM accuracy in high-dimensional domain.
(2) A no-reference image blurring metric. Based on the edge gradient, the degree of image blur can be measured.
(3) A no-reference color IQM based upon colorfulness, contrast and sharpness
Modeling high-entropy transition-metal alloys with alchemical compression
Alloys composed of several elements in roughly equimolar composition, often
referred to as high-entropy alloys, have long been of interest for their
thermodynamics and peculiar mechanical properties, and more recently for their
potential application in catalysis. They are a considerable challenge to
traditional atomistic modeling, and also to data-driven potentials that for the
most part have memory footprint, computational effort and data requirements
which scale poorly with the number of elements included. We apply a recently
proposed scheme to compress chemical information in a lower-dimensional space,
which reduces dramatically the cost of the model with negligible loss of
accuracy, to build a potential that can describe 25 d-block transition metals.
The model shows semi-quantitative accuracy for prototypical alloys, and is
remarkably stable when extrapolating to structures outside its training set. We
use this framework to study element segregation in a computational experiment
that simulates an equimolar alloy of all 25 elements, mimicking the seminal
experiments by Cantor et al., and use our observations on the short-range order
relations between the elements to define a data-driven set of Hume-Rothery
rules that can serve as guidance for alloy design. We conclude with a study of
three prototypical alloys, CoCrFeMnNi, CoCrFeMoNi and IrPdPtRhRu, determining
their stability and the short-range order behavior of their constituents
Cortico-subcortical functional connectivity profiles of resting-state networks in marmosets and humans
Copyright © 2020 the authors Understanding the similarity of cortico-subcortical networks topologies between humans and nonhuman primate species is critical to study the origin of network alternations underlying human neurologic and neuropsychiatric diseases. The New World common marmoset (Callithrix jacchus) has become popular as a nonhuman primate model for human brain function. Most marmoset connectomic research, however, has exclusively focused on cortical areas, with connectivity to subcortical networks less extensively explored. Here, we aimed to first isolate patterns of subcortical connectivity with cortical resting-state networks in awake marmosets using resting-state fMRI, then to compare these networks with those in humans using connectivity fingerprinting. In this study, we used 5 marmosets (4 males, 1 female). While we could match several marmoset and human resting-state networks based on their functional fingerprints, we also found a few striking differences, for example, strong functional connectivity of the default mode network with the superior colliculus in marmosets that was much weaker in humans. Together, these findings demonstrate that many of the core cortico-subcortical networks in humans are also present in marmosets, but that small, potentially functionally relevant differences exist
Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models
In this study, we present an efficient and effective approach for achieving
temporally consistent synthetic-to-real video translation in videos of varying
lengths. Our method leverages off-the-shelf conditional image diffusion models,
allowing us to perform multiple synthetic-to-real image generations in
parallel. By utilizing the available optical flow information from the
synthetic videos, our approach seamlessly enforces temporal consistency among
corresponding pixels across frames. This is achieved through joint noise
optimization, effectively minimizing spatial and temporal discrepancies. To the
best of our knowledge, our proposed method is the first to accomplish diverse
and temporally consistent synthetic-to-real video translation using conditional
image diffusion models. Furthermore, our approach does not require any training
or fine-tuning of the diffusion models. Extensive experiments conducted on
various benchmarks for synthetic-to-real video translation demonstrate the
effectiveness of our approach, both quantitatively and qualitatively. Finally,
we show that our method outperforms other baseline methods in terms of both
temporal consistency and visual quality
DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion
Denosing diffusion model, as a generative model, has received a lot of
attention in the field of image generation recently, thanks to its powerful
generation capability. However, diffusion models have not yet received
sufficient research in the field of image fusion. In this article, we introduce
diffusion model to the image fusion field, treating the image fusion task as
image-to-image translation and designing two different conditional injection
modulation modules (i.e., style transfer modulation and wavelet modulation) to
inject coarse-grained style information and fine-grained high-frequency and
low-frequency information into the diffusion UNet, thereby generating fused
images. In addition, we also discussed the residual learning and the selection
of training objectives of the diffusion model in the image fusion task.
Extensive experimental results based on quantitative and qualitative
assessments compared with benchmarks demonstrates state-of-the-art results and
good generalization performance in image fusion tasks. Finally, it is hoped
that our method can inspire other works and gain insight into this field to
better apply the diffusion model to image fusion tasks. Code shall be released
for better reproducibility
Multi-Layered Paper-Based Microfluidic Platform For Point-Of-Care Male Fertility Test
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