Modular Degradation Simulation and Restoration for Under-Display Camera

• URL: http://arxiv.org/abs/2209.11455v1
• Date: Fri, 23 Sep 2022 07:36:07 GMT
• Title: Modular Degradation Simulation and Restoration for Under-Display Camera
• Authors: Yang Zhou, Yuda Song, Xin Du
• Abstract summary: Under-display camera (UDC) provides an elegant solution for full-screen smartphones. UDC captured images suffer from severe degradation since sensors lie under the display. We propose a modular network dubbed MPGNet trained using the generative adversarial network (GAN) framework for simulating UDC imaging.
• Score: 21.048590332029995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Under-display camera (UDC) provides an elegant solution for full-screen smartphones. However, UDC captured images suffer from severe degradation since sensors lie under the display. Although this issue can be tackled by image restoration networks, these networks require large-scale image pairs for training. To this end, we propose a modular network dubbed MPGNet trained using the generative adversarial network (GAN) framework for simulating UDC imaging. Specifically, we note that the UDC imaging degradation process contains brightness attenuation, blurring, and noise corruption. Thus we model each degradation with a characteristic-related modular network, and all modular networks are cascaded to form the generator. Together with a pixel-wise discriminator and supervised loss, we can train the generator to simulate the UDC imaging degradation process. Furthermore, we present a Transformer-style network named DWFormer for UDC image restoration. For practical purposes, we use depth-wise convolution instead of the multi-head self-attention to aggregate local spatial information. Moreover, we propose a novel channel attention module to aggregate global information, which is critical for brightness recovery. We conduct evaluations on the UDC benchmark, and our method surpasses the previous state-of-the-art models by 1.23 dB on the P-OLED track and 0.71 dB on the T-OLED track, respectively.

Related papers

• Pixel-Aligned Multi-View Generation with Depth Guided Decoder [86.1813201212539]
  We propose a novel method for pixel-level image-to-multi-view generation. Unlike prior work, we incorporate attention layers across multi-view images in the VAE decoder of a latent video diffusion model. Our model enables better pixel alignment across multi-view images.
  arXiv  Detail & Related papers  (2024-08-26T04:56:41Z)
• Segmentation Guided Sparse Transformer for Under-Display Camera Image Restoration [91.65248635837145]
  Under-Display Camera (UDC) is an emerging technology that achieves full-screen display via hiding the camera under the display panel. In this paper, we observe that when using the Vision Transformer for UDC degraded image restoration, the global attention samples a large amount of redundant information and noise. We propose a Guided Sparse Transformer method (SGSFormer) for the task of restoring high-quality images from UDC degraded images.
  arXiv  Detail & Related papers  (2024-03-09T13:11:59Z)
• AoSRNet: All-in-One Scene Recovery Networks via Multi-knowledge Integration [17.070755601209136]
  We propose an all-in-one scene recovery network via multi-knowledge integration (termed AoSRNet) It combines gamma correction (GC) and optimized linear stretching (OLS) to create the detail enhancement module (DEM) and color restoration module ( CRM) Comprehensive experimental results demonstrate the effectiveness and stability of AoSRNet compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-06T06:12:03Z)
• DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement [77.0360085530701]
  Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. Previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features. Our approach utilizes predicted images to dynamically update pseudo-labels, adding a dynamic gradient to optimize the network's gradient space.
  arXiv  Detail & Related papers  (2023-12-12T06:07:21Z)
• Deep Video Restoration for Under-Display Camera [98.17505013737446]
  We propose a GAN-based generation pipeline to simulate the realistic UDC degradation process. We build the first large-scale UDC video restoration dataset called PexelsUDC. We propose a novel transformer-based baseline method that adaptively enhances degraded videos.
  arXiv  Detail & Related papers  (2023-09-09T10:48:06Z)
• Generating Aligned Pseudo-Supervision from Non-Aligned Data for Image Restoration in Under-Display Camera [84.41316720913785]
  We revisit the classic stereo setup for training data collection -- capturing two images of the same scene with one UDC and one standard camera. The key idea is to "copy" details from a high-quality reference image and "paste" them on the UDC image. A novel Transformer-based framework generates well-aligned yet high-quality target data for the corresponding UDC input.
  arXiv  Detail & Related papers  (2023-04-12T17:56:42Z)
• UDC-UNet: Under-Display Camera Image Restoration via U-Shape Dynamic Network [13.406025621307132]
  Under-Display Camera (UDC) has been widely exploited to help smartphones realize full screen display. As the screen could inevitably affect the light propagation process, the images captured by the UDC system usually contain flare, haze, blur, and noise. In this paper, we propose a new deep model, namely UDC-UNet, to address the UDC image restoration problem with the known Point Spread Function (PSF) in HDR scenes.
  arXiv  Detail & Related papers  (2022-09-05T07:41:44Z)
• Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Network [80.67717076541956]
  Under-Display Camera (UDC) systems provide a true bezel-less and notch-free viewing experience on smartphones. In a typical UDC system, the pixel array attenuates and diffracts the incident light on the camera, resulting in significant image quality degradation. In this work, we aim to analyze and tackle the aforementioned degradation problems.
  arXiv  Detail & Related papers  (2021-04-19T18:41:45Z)
• Deep Atrous Guided Filter for Image Restoration in Under Display Cameras [18.6418313982586]
  Under Display Cameras present a promising opportunity for phone manufacturers to achieve bezel-free displays by positioning the camera behind semi-transparent OLED screens. Such imaging systems suffer from severe image degradation due to light attenuation and diffraction effects. We present Deep Atrous Guided Filter (DAGF), a two-stage, end-to-end approach for image restoration in UDC systems.
  arXiv  Detail & Related papers  (2020-08-14T07:54:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.