Video Dehazing via a Multi-Range Temporal Alignment Network with
Physical Prior
- URL: http://arxiv.org/abs/2303.09757v1
- Date: Fri, 17 Mar 2023 03:44:17 GMT
- Title: Video Dehazing via a Multi-Range Temporal Alignment Network with
Physical Prior
- Authors: Jiaqi Xu, Xiaowei Hu, Lei Zhu, Qi Dou, Jifeng Dai, Yu Qiao, Pheng-Ann
Heng
- Abstract summary: Video dehazing aims to recover haze-free frames with high visibility and contrast.
This paper presents a novel framework to explore the physical haze priors and aggregate temporal information.
We construct the first large-scale outdoor video dehazing benchmark dataset.
- Score: 117.6741444489174
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Video dehazing aims to recover haze-free frames with high visibility and
contrast. This paper presents a novel framework to effectively explore the
physical haze priors and aggregate temporal information. Specifically, we
design a memory-based physical prior guidance module to encode the
prior-related features into long-range memory. Besides, we formulate a
multi-range scene radiance recovery module to capture space-time dependencies
in multiple space-time ranges, which helps to effectively aggregate temporal
information from adjacent frames. Moreover, we construct the first large-scale
outdoor video dehazing benchmark dataset, which contains videos in various
real-world scenarios. Experimental results on both synthetic and real
conditions show the superiority of our proposed method.
Related papers
- Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance [24.671417176179187]
Real driving-video dehazing poses a significant challenge due to the inherent difficulty in acquiring precisely aligned/clear video pairs.
We propose a pioneering approach that addresses this challenge through a nonaligned regularization strategy.
Our approach comprises two key components: reference matching and video dehazing.
arXiv Detail & Related papers (2024-05-16T11:28:01Z) - A Simple Recipe for Contrastively Pre-training Video-First Encoders
Beyond 16 Frames [54.90226700939778]
We build on the common paradigm of transferring large-scale, image--text models to video via shallow temporal fusion.
We expose two limitations to the approach: (1) decreased spatial capabilities, likely due to poor video--language alignment in standard video datasets, and (2) higher memory consumption, bottlenecking the number of frames that can be processed.
arXiv Detail & Related papers (2023-12-12T16:10:19Z) - Continuous Space-Time Video Super-Resolution Utilizing Long-Range
Temporal Information [48.20843501171717]
We propose a continuous ST-VSR (CSTVSR) method that can convert the given video to any frame rate and spatial resolution.
We show that the proposed algorithm has good flexibility and achieves better performance on various datasets.
arXiv Detail & Related papers (2023-02-26T08:02:39Z) - Fast Non-Rigid Radiance Fields from Monocularized Data [66.74229489512683]
This paper proposes a new method for full 360deg inward-facing novel view synthesis of non-rigidly deforming scenes.
At the core of our method are 1) An efficient deformation module that decouples the processing of spatial and temporal information for accelerated training and inference; and 2) A static module representing the canonical scene as a fast hash-encoded neural radiance field.
In both cases, our method is significantly faster than previous methods, converging in less than 7 minutes and achieving real-time framerates at 1K resolution, while obtaining a higher visual accuracy for generated novel views.
arXiv Detail & Related papers (2022-12-02T18:51:10Z) - Motion-aware Memory Network for Fast Video Salient Object Detection [15.967509480432266]
We design a space-time memory (STM)-based network, which extracts useful temporal information of the current frame from adjacent frames as the temporal branch of VSOD.
In the encoding stage, we generate high-level temporal features by using high-level features from the current and its adjacent frames.
In the decoding stage, we propose an effective fusion strategy for spatial and temporal branches.
The proposed model does not require optical flow or other preprocessing, and can reach a speed of nearly 100 FPS during inference.
arXiv Detail & Related papers (2022-08-01T15:56:19Z) - Video Demoireing with Relation-Based Temporal Consistency [68.20281109859998]
Moire patterns, appearing as color distortions, severely degrade image and video qualities when filming a screen with digital cameras.
We study how to remove such undesirable moire patterns in videos, namely video demoireing.
arXiv Detail & Related papers (2022-04-06T17:45:38Z) - Coarse-Fine Networks for Temporal Activity Detection in Videos [45.03545172714305]
We introduce 'Co-Fine Networks', a two-stream architecture which benefits from different abstractions of temporal resolution to learn better video representations for long-term motion.
We show that our method can outperform the state-of-the-arts for action detection in public datasets with a significantly reduced compute and memory footprint.
arXiv Detail & Related papers (2021-03-01T20:48:01Z) - Video Super-resolution with Temporal Group Attention [127.21615040695941]
We propose a novel method that can effectively incorporate temporal information in a hierarchical way.
The input sequence is divided into several groups, with each one corresponding to a kind of frame rate.
It achieves favorable performance against state-of-the-art methods on several benchmark datasets.
arXiv Detail & Related papers (2020-07-21T04:54:30Z)
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.