Let's See Clearly: Contaminant Artifact Removal for Moving Cameras

04/18/2021
by   Xiaoyu Li, et al.
1

Contaminants such as dust, dirt and moisture adhering to the camera lens can greatly affect the quality and clarity of the resulting image or video. In this paper, we propose a video restoration method to automatically remove these contaminants and produce a clean video. Our approach first seeks to detect attention maps that indicate the regions that need to be restored. In order to leverage the corresponding clean pixels from adjacent frames, we propose a flow completion module to hallucinate the flow of the background scene to the attention regions degraded by the contaminants. Guided by the attention maps and completed flows, we propose a recurrent technique to restore the input frame by fetching clean pixels from adjacent frames. Finally, a multi-frame processing stage is used to further process the entire video sequence in order to enforce temporal consistency. The entire network is trained on a synthetic dataset that approximates the physical lighting properties of contaminant artifacts. This new dataset and our novel framework lead to our method that is able to address different contaminants and outperforms competitive restoration approaches both qualitatively and quantitatively.

READ FULL TEXT

page 1

page 3

page 4

page 6

page 7

page 8

research
05/29/2022

Feature-Aligned Video Raindrop Removal with Temporal Constraints

Existing adherent raindrop removal methods focus on the detection of the...
research
01/25/2023

Efficient Flow-Guided Multi-frame De-fencing

Taking photographs ”in-the-wild” is often hindered by fence obstructions...
research
04/19/2021

Restoration of Video Frames from a Single Blurred Image with Motion Understanding

We propose a novel framework to generate clean video frames from a singl...
research
07/08/2021

Multi-frame Collaboration for Effective Endoscopic Video Polyp Detection via Spatial-Temporal Feature Transformation

Precise localization of polyp is crucial for early cancer screening in g...
research
03/09/2020

Restore from Restored: Video Restoration with Pseudo Clean Video

In this paper, we propose a self-supervised video denoising method calle...
research
11/28/2017

Attentive Generative Adversarial Network for Raindrop Removal from a Single Image

Raindrops adhered to a glass window or camera lens can severely hamper t...
research
10/18/2022

MotionDeltaCNN: Sparse CNN Inference of Frame Differences in Moving Camera Videos

Convolutional neural network inference on video input is computationally...

Please sign up or login with your details

Forgot password? Click here to reset