Enhanced Spatial Awareness for Deep Interactive Image Segmentation

Li, Haochen; Ni, Jianjun; Li, Zhicheng; Qian, Yuxiang; Wang, Tao

doi:10.1007/978-3-031-18916-6_40

Cited by 1 publication

(1 citation statement)

References 34 publications

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“…2(b), to guide the removal of diffraction (Feng et al 2021;Kwon et al 2021;Liu et al 2022c). Another part directly learns diffraction removal through complex network design (Feng et al 2023;Koh, Lee, and Yoon 2022;Liu et al 2023a). Unfortunately, unlike UDC images, diffraction will change dynamically with motion in UDC video.…”

Section: Flare Map Haze Mapmentioning

confidence: 99%

Decoupling Degradations with Recurrent Network for Video Restoration in Under-Display Camera

Liu,

Wang,

Fan

et al. 2024

AAAI

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Under-display camera (UDC) systems are the foundation of full-screen display devices in which the lens mounts under the display. The pixel array of light-emitting diodes used for display diffracts and attenuates incident light, causing various degradations as the light intensity changes. Unlike general video restoration which recovers video by treating different degradation factors equally, video restoration for UDC systems is more challenging that concerns removing diverse degradation over time while preserving temporal consistency. In this paper, we introduce a novel video restoration network, called D2RNet, specifically designed for UDC systems. It employs a set of Decoupling Attention Modules (DAM) that effectively separate the various video degradation factors. More specifically, a soft mask generation function is proposed to formulate each frame into flare and haze based on the diffraction arising from incident light of different intensities, followed by the proposed flare and haze removal components that leverage long- and short-term feature learning to handle the respective degradations. Such a design offers an targeted and effective solution to eliminating various types of degradation in UDC systems. We further extend our design into multi-scale to overcome the scale-changing of degradation that often occur in long-range videos. To demonstrate the superiority of D2RNet, we propose a large-scale UDC video benchmark by gathering HDR videos and generating realistically degraded videos using the point spread function measured by a commercial UDC system. Extensive quantitative and qualitative evaluations demonstrate the superiority of D2RNet compared to other state-of-the-art video restoration and UDC image restoration methods.

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