2,481 research outputs found
Color Constancy Using CNNs
In this work we describe a Convolutional Neural Network (CNN) to accurately
predict the scene illumination. Taking image patches as input, the CNN works in
the spatial domain without using hand-crafted features that are employed by
most previous methods. The network consists of one convolutional layer with max
pooling, one fully connected layer and three output nodes. Within the network
structure, feature learning and regression are integrated into one optimization
process, which leads to a more effective model for estimating scene
illumination. This approach achieves state-of-the-art performance on a standard
dataset of RAW images. Preliminary experiments on images with spatially varying
illumination demonstrate the stability of the local illuminant estimation
ability of our CNN.Comment: Accepted at DeepVision: Deep Learning in Computer Vision 2015 (CVPR
2015 workshop
Pushing the Limits of 3D Color Printing: Error Diffusion with Translucent Materials
Accurate color reproduction is important in many applications of 3D printing,
from design prototypes to 3D color copies or portraits. Although full color is
available via other technologies, multi-jet printers have greater potential for
graphical 3D printing, in terms of reproducing complex appearance properties.
However, to date these printers cannot produce full color, and doing so poses
substantial technical challenges, from the shear amount of data to the
translucency of the available color materials. In this paper, we propose an
error diffusion halftoning approach to achieve full color with multi-jet
printers, which operates on multiple isosurfaces or layers within the object.
We propose a novel traversal algorithm for voxel surfaces, which allows the
transfer of existing error diffusion algorithms from 2D printing. The resulting
prints faithfully reproduce colors, color gradients and fine-scale details.Comment: 15 pages, 14 figures; includes supplemental figure
Appearance-based image splitting for HDR display systems
High dynamic range displays that incorporate two optically-coupled image planes have recently been developed. This dual image plane design requires that a given HDR input image be split into two complementary standard dynamic range components that drive the coupled systems, therefore there existing image splitting issue. In this research, two types of HDR display systems (hardcopy and softcopy HDR display) are constructed to facilitate the study of HDR image splitting algorithm for building HDR displays. A new HDR image splitting algorithm which incorporates iCAM06 image appearance model is proposed, seeking to create displayed HDR images that can provide better image quality. The new algorithm has potential to improve image details perception, colorfulness and better gamut utilization. Finally, the performance of the new iCAM06-based HDR image splitting algorithm is evaluated and compared with widely spread luminance square root algorithm through psychophysical studies
Quasiconvex Programming
We define quasiconvex programming, a form of generalized linear programming
in which one seeks the point minimizing the pointwise maximum of a collection
of quasiconvex functions. We survey algorithms for solving quasiconvex programs
either numerically or via generalizations of the dual simplex method from
linear programming, and describe varied applications of this geometric
optimization technique in meshing, scientific computation, information
visualization, automated algorithm analysis, and robust statistics.Comment: 33 pages, 14 figure
Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation
Modern displays are capable of rendering video content with high dynamic
range (HDR) and wide color gamut (WCG). However, the majority of available
resources are still in standard dynamic range (SDR). As a result, there is
significant value in transforming existing SDR content into the HDRTV standard.
In this paper, we define and analyze the SDRTV-to-HDRTV task by modeling the
formation of SDRTV/HDRTV content. Our analysis and observations indicate that a
naive end-to-end supervised training pipeline suffers from severe gamut
transition errors. To address this issue, we propose a novel three-step
solution pipeline called HDRTVNet++, which includes adaptive global color
mapping, local enhancement, and highlight refinement. The adaptive global color
mapping step uses global statistics as guidance to perform image-adaptive color
mapping. A local enhancement network is then deployed to enhance local details.
Finally, we combine the two sub-networks above as a generator and achieve
highlight consistency through GAN-based joint training. Our method is primarily
designed for ultra-high-definition TV content and is therefore effective and
lightweight for processing 4K resolution images. We also construct a dataset
using HDR videos in the HDR10 standard, named HDRTV1K that contains 1235 and
117 training images and 117 testing images, all in 4K resolution. Besides, we
select five metrics to evaluate the results of SDRTV-to-HDRTV algorithms. Our
final results demonstrate state-of-the-art performance both quantitatively and
visually. The code, model and dataset are available at
https://github.com/xiaom233/HDRTVNet-plus.Comment: Extended version of HDRTVNe
A survey of outlier detection methodologies
Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review
Evaluation of the color image and video processing chain and visual quality management for consumer systems
With the advent of novel digital display technologies, color processing is increasingly becoming a key aspect in consumer video applications. Today’s state-of-the-art displays require sophisticated color and image reproduction techniques in order to achieve larger screen size, higher luminance and higher resolution than ever before. However, from color science perspective, there are clearly opportunities for improvement in the color reproduction capabilities of various emerging and conventional display technologies. This research seeks to identify potential areas for improvement in color processing in a video processing chain. As part of this research, various processes involved in a typical video processing chain in consumer video applications were reviewed. Several published color and contrast enhancement algorithms were evaluated, and a novel algorithm was developed to enhance color and contrast in images and videos in an effective and coordinated manner. Further, a psychophysical technique was developed and implemented for performing visual evaluation of color image and consumer video quality. Based on the performance analysis and visual experiments involving various algorithms, guidelines were proposed for the development of an effective color and contrast enhancement method for images and video applications. It is hoped that the knowledge gained from this research will help build a better understanding of color processing and color quality management methods in consumer video
Pixel Arrangement and Mapping Algorithm for Improving Saturation and Brightness
With the increasing demand for display effects and resolution in the display market, the traditional RGBW permutation has defects such as reduced display saturation and poor image restoration. A new sub-pixel arrangement is designed for these defects, reducing the number of white pixels W and adding yellow pixels Y, which reduces the influence of white pixels on surrounding pixels compared to the conventional RGBW structure. According to the new arrangement method, two new mapping algorithms are designed, and the saturation concept is introduced. The yellow component and the white component are adjusted by the saturation change to effectively improve the image saturation. The simulation shows that the new mapping algorithm (1) has a higher degree of restoration on the image and increases the saturation by 10%. The new mapping algorithm (2) can improve the brightness based on maintaining the high saturation of the original image
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