Rain Removal in Video by Combining Temporal and Chromatic Properties

By Xiaopeng Zhang, Hao Li, Yingyi Qi, Wee Kheng Leow, Teck Khim Ng. IEEE International Conference on Multimedia and Expo, 2006

307 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/094dffafa3e2220ae6db6f76ffa8246723236da9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Joint Bi-layer Optimization for Single-Image Rain Streak Removal (2017, background): “Video-based methods [11, 34, 1, 2, 24, 33] leverage the rich temporal information across frames to locate and remove rain streaks, while single-image methods [15, 16, 5, 26, 14, 21, 19] require the use of image priors to recover the underlying background scene, e.”
Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning (2019, background): “This applies to all the image-based [16, 24, 14, 22, 38, 7, 40, 21] and video-based works [41, 8, 1, 3, 23, 17, 6, 19, 31, 5, 32, 33, 39].”
Analysis of Rain and Snow in Frequency Space (2008, methodology): “Temporal median filtering is the simplest method for cleaning videos (Hase et al. 1999; Starik and Werman 2003).”
Image De-Raining Transformer (2022, background): “To alleviate the impact of rainy artifacts, early researches focus on video de-raining [1], [2], [3], [4], [5], [6], [7] by exploiting the temporal consistency between successive frames as a constraint.”
Image De-Raining Using a Conditional Generative Adversarial Network (2017, unknown): “In the case of video-based de-raining, a common strategy to solve (1) is to use additional temporal information, such as methods proposed in [5]–[7].”
Rain Streak Removal Using Layer Priors (2016, background): “[27] added an additional constraint called the chromaticity constraint.”
Single Image Deraining: A Comprehensive Benchmark Analysis (2019, background): “Multi-frame based approaches: Early methods often require multiple frames to deal with the deraining problem [4, 16, 17, 18, 19, 5, 20, 11].”
Joint Rain Detection and Removal from a Single Image with Contextualized Deep Networks (2020, background): “Zhang et al. [7] and Brewer et al. [8] focus on the chromaticity and shape of rain streaks, respectively.”
Removing Rain from a Single Image via Discriminative Sparse Coding (2015, background): “[28] proposed two rain priors to detect the rain regions.”
Deep Joint Rain Detection and Removal from a Single Image (2016, background): “Some focus on rain image recovery from video sequences [3, 4, 5, 9, 13, 14, 15, 16, 44].”
A Generalized Low-Rank Appearance Model for Spatio-temporally Correlated Rain Streaks (2013, methodology): “Finally, as pointed out in [5], raindrops bring similar changes for three color channels at different pixels.”
A Model-Driven Deep Neural Network for Single Image Rain Removal (2020, methodology): “Later, both temporal and chromatic properties of rain were considered and then background layer was extracted from rainy video by utilizing different strategies such as K-means clustering [55], Kalman filter [33], and GMM [3].”