45,569 research outputs found
Blind Face Restoration for Under-Display Camera via Dictionary Guided Transformer
By hiding the front-facing camera below the display panel, Under-Display
Camera (UDC) provides users with a full-screen experience. However, due to the
characteristics of the display, images taken by UDC suffer from significant
quality degradation. Methods have been proposed to tackle UDC image restoration
and advances have been achieved. There are still no specialized methods and
datasets for restoring UDC face images, which may be the most common problem in
the UDC scene. To this end, considering color filtering, brightness
attenuation, and diffraction in the imaging process of UDC, we propose a
two-stage network UDC Degradation Model Network named UDC-DMNet to synthesize
UDC images by modeling the processes of UDC imaging. Then we use UDC-DMNet and
high-quality face images from FFHQ and CelebA-Test to create UDC face training
datasets FFHQ-P/T and testing datasets CelebA-Test-P/T for UDC face
restoration. We propose a novel dictionary-guided transformer network named
DGFormer. Introducing the facial component dictionary and the characteristics
of the UDC image in the restoration makes DGFormer capable of addressing blind
face restoration in UDC scenarios. Experiments show that our DGFormer and
UDC-DMNet achieve state-of-the-art performance
Under-Display Camera Image Restoration with Scattering Effect
The under-display camera (UDC) provides consumers with a full-screen visual
experience without any obstruction due to notches or punched holes. However,
the semi-transparent nature of the display inevitably introduces the severe
degradation into UDC images. In this work, we address the UDC image restoration
problem with the specific consideration of the scattering effect caused by the
display. We explicitly model the scattering effect by treating the display as a
piece of homogeneous scattering medium. With the physical model of the
scattering effect, we improve the image formation pipeline for the image
synthesis to construct a realistic UDC dataset with ground truths. To suppress
the scattering effect for the eventual UDC image recovery, a two-branch
restoration network is designed. More specifically, the scattering branch
leverages global modeling capabilities of the channel-wise self-attention to
estimate parameters of the scattering effect from degraded images. While the
image branch exploits the local representation advantage of CNN to recover
clear scenes, implicitly guided by the scattering branch. Extensive experiments
are conducted on both real-world and synthesized data, demonstrating the
superiority of the proposed method over the state-of-the-art UDC restoration
techniques. The source code and dataset are available at
\url{https://github.com/NamecantbeNULL/SRUDC}.Comment: Accepted to ICCV202
Real-Time Under-Display Cameras Image Restoration and HDR on Mobile Devices
The new trend of full-screen devices implies positioning the camera behind
the screen to bring a larger display-to-body ratio, enhance eye contact, and
provide a notch-free viewing experience on smartphones, TV or tablets. On the
other hand, the images captured by under-display cameras (UDCs) are degraded by
the screen in front of them. Deep learning methods for image restoration can
significantly reduce the degradation of captured images, providing satisfying
results for the human eyes. However, most proposed solutions are unreliable or
efficient enough to be used in real-time on mobile devices.
In this paper, we aim to solve this image restoration problem using efficient
deep learning methods capable of processing FHD images in real-time on
commercial smartphones while providing high-quality results. We propose a
lightweight model for blind UDC Image Restoration and HDR, and we also provide
a benchmark comparing the performance and runtime of different methods on
smartphones. Our models are competitive on UDC benchmarks while using x4 less
operations than others. To the best of our knowledge, we are the first work to
approach and analyze this real-world single image restoration problem from the
efficiency and production point of view.Comment: ECCV 2022 AIM Worksho
Assessment of plastics in the National Trust: a case study at Mr Straw's House
The National Trust is a charity that cares for over 300 publically accessible historic buildings and their contents across England, Wales and Northern Ireland. There have been few previous studies on preservation of plastics within National Trust collections, which form a significant part of the more modern collections of objects. This paper describes the design of an assessment system which was successfully trialled at Mr Straws House, a National Trust property in Worksop, UK. This system can now be used for future plastic surveys at other National Trust properties. In addition, the survey gave valuable information about the state of the collection, demonstrating that the plastics that are deteriorating are those that are known to be vulnerable, namely cellulose nitrate/acetate, PVC and rubber. Verifying this knowledge of the most vulnerable plastics enables us to recommend to properties across National Trust that these types should be seen as a priority for correct storage and in-depth recording
Man-machine interactive imaging and data processing using high-speed digital mass storage
The role of vision in teleoperation has been recognized as an important element in the man-machine control loop. In most applications of remote manipulation, direct vision cannot be used. To overcome this handicap, the human operator's control capabilities are augmented by a television system. This medium provides a practical and useful link between workspace and the control station from which the operator perform his tasks. Human performance deteriorates when the images are degraded as a result of instrumental and transmission limitations. Image enhancement is used to bring out selected qualities in a picture to increase the perception of the observer. A general purpose digital computer, an extensive special purpose software system is used to perform an almost unlimited repertoire of processing operations
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