12 research outputs found
Recaptured Raw Screen Image and Video Demoir\'eing via Channel and Spatial Modulations
Capturing screen contents by smartphone cameras has become a common way for
information sharing. However, these images and videos are often degraded by
moir\'e patterns, which are caused by frequency aliasing between the camera
filter array and digital display grids. We observe that the moir\'e patterns in
raw domain is simpler than those in sRGB domain, and the moir\'e patterns in
raw color channels have different properties. Therefore, we propose an image
and video demoir\'eing network tailored for raw inputs. We introduce a
color-separated feature branch, and it is fused with the traditional
feature-mixed branch via channel and spatial modulations. Specifically, the
channel modulation utilizes modulated color-separated features to enhance the
color-mixed features. The spatial modulation utilizes the feature with large
receptive field to modulate the feature with small receptive field. In
addition, we build the first well-aligned raw video demoir\'eing
(RawVDemoir\'e) dataset and propose an efficient temporal alignment method by
inserting alternating patterns. Experiments demonstrate that our method
achieves state-of-the-art performance for both image and video demori\'eing. We
have released the code and dataset in https://github.com/tju-chengyijia/VD_raw
Semantic Photo Manipulation with a Generative Image Prior
Despite the recent success of GANs in synthesizing images conditioned on
inputs such as a user sketch, text, or semantic labels, manipulating the
high-level attributes of an existing natural photograph with GANs is
challenging for two reasons. First, it is hard for GANs to precisely reproduce
an input image. Second, after manipulation, the newly synthesized pixels often
do not fit the original image. In this paper, we address these issues by
adapting the image prior learned by GANs to image statistics of an individual
image. Our method can accurately reconstruct the input image and synthesize new
content, consistent with the appearance of the input image. We demonstrate our
interactive system on several semantic image editing tasks, including
synthesizing new objects consistent with background, removing unwanted objects,
and changing the appearance of an object. Quantitative and qualitative
comparisons against several existing methods demonstrate the effectiveness of
our method.Comment: SIGGRAPH 201
Efficient Halftoning via Deep Reinforcement Learning
Halftoning aims to reproduce a continuous-tone image with pixels whose
intensities are constrained to two discrete levels. This technique has been
deployed on every printer, and the majority of them adopt fast methods (e.g.,
ordered dithering, error diffusion) that fail to render structural details,
which determine halftone's quality. Other prior methods of pursuing visual
pleasure by searching for the optimal halftone solution, on the contrary,
suffer from their high computational cost. In this paper, we propose a fast and
structure-aware halftoning method via a data-driven approach. Specifically, we
formulate halftoning as a reinforcement learning problem, in which each binary
pixel's value is regarded as an action chosen by a virtual agent with a shared
fully convolutional neural network (CNN) policy. In the offline phase, an
effective gradient estimator is utilized to train the agents in producing
high-quality halftones in one action step. Then, halftones can be generated
online by one fast CNN inference. Besides, we propose a novel anisotropy
suppressing loss function, which brings the desirable blue-noise property.
Finally, we find that optimizing SSIM could result in holes in flat areas,
which can be avoided by weighting the metric with the contone's contrast map.
Experiments show that our framework can effectively train a light-weight CNN,
which is 15x faster than previous structure-aware methods, to generate
blue-noise halftones with satisfactory visual quality. We also present a
prototype of deep multitoning to demonstrate the extensibility of our method
Digital imaging technology assessment: Digital document storage project
An ongoing technical assessment and requirements definition project is examining the potential role of digital imaging technology at NASA's STI facility. The focus is on the basic components of imaging technology in today's marketplace as well as the components anticipated in the near future. Presented is a requirement specification for a prototype project, an initial examination of current image processing at the STI facility, and an initial summary of image processing projects at other sites. Operational imaging systems incorporate scanners, optical storage, high resolution monitors, processing nodes, magnetic storage, jukeboxes, specialized boards, optical character recognition gear, pixel addressable printers, communications, and complex software processes