Analysis of Rain and Snow in Frequency Space

By Peter C. Barnum, Srinivasa G. Narasimhan, Takeo Kanade. International Journal of Computer Vision, 2008

369 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/062d8510ccfedb0f9f4783c00c396bf43f8bb990

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In Computer Vision and Pattern Recognition

Citations in the corpus

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].”
Lightweight Pyramid Networks for Image Deraining (2018, methodology): “Other methods focus on deraining in the Fourier domain [2], using Gaussian mixture models (GMMs) [3], low-rank approximations [4], and via matrix completions [5].”
Single Image Deraining: A Comprehensive Benchmark Analysis (2019, background): “Prior based algorithms: Many deraining methods capitalize on clean image or rain type priors to remove rain [22, 1, 23, 24, 25].”
A Review of Computer Vision Techniques for the Analysis of Urban Traffic (2011, background): “However, there is a significant body of work that deals with image restoration under these conditions [10], [54], [134].”
Removing Rain from a Single Image via Discriminative Sparse Coding (2015, methodology): “For re-covering images taken in rainy weather, most of the work has been focusing on video or data with multiple frames [3, 10].”
Multi-Scale Progressive Fusion Network for Single Image Deraining (2020, background): “Traditional deraining methods [2, 1, 5, 9, 32] use simple linear-mapping transformations and are not robust to variations of the input [11], e.g ., rain streaks with various directions, densities and sizes.”
A Model-Driven Deep Neural Network for Single Image Rain Removal (2020, methodology): “Besides, a spatio-temporal frequency based raindrop detection method was provided in [1].”
A Generalized Low-Rank Appearance Model for Spatio-temporally Correlated Rain Streaks (2013, methodology): “We use three video sequences, including the “heavy rain” released by [2], the “mag” released by [3], and the “forrest” released by [7], to evaluate our method.”
Removing Rain from Single Images via a Deep Detail Network (2017, methodology): “For video-based methods, rain can be more easily identified and removed using inter-frame information [3,4,10,21, 28, 35].”
Rain Removal in Traffic Surveillance: Does it Matter? (2018, methodology): “Other metrics include the forward-backward feature tracking accuracy [19], the measurement of image variance [46], and the comparison of face detection scores on original and rain-removed images [32].”
Joint Bi-layer Optimization for Single-Image Rain Streak Removal (2017, methodology): “Other methods include [1, 2, 24]; see [28] for a detailed review.”
Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal (2016, background): “In [2], the authors analyze the properties of rain and establish a model of visual effect of rain in frequency space.”
Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining (2018, background): “As video based methods can leverage the temporal information by analyzing the difference between adjacent frames, it is relatively easy to remove the rain from videos [3,14].”
Semi-Supervised Transfer Learning for Image Rain Removal (2018, background): “Early video derain methods [13, 2, 3, 18] designed many useful techniques to detect potential rain streaks based on their physical characteristics and removed these detected rain by image restoration algorithms.”
Depth-Attentional Features for Single-Image Rain Removal (2019, methodology): “Early methods [1, 19, 22, 32, 41] remove rain streaks in images by designing hand-crafted priors based on lowlevel image statistics.”
Joint Rain Detection and Removal from a Single Image with Contextualized Deep Networks (2020, methodology): “Some focus on video deraining [3], [4], [5], [6], [7], [8], [9], [10], [11].”