Rolling Guidance Filter
By Xiaoyong Shen, Li Xu, Jiaya Jia. 2014
585 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Ming-Hsuan Yang
- Luc Van Gool
- Dacheng Tao
- Chen Change Loy
- Xiaogang Wang
- Vladlen Koltun
- Andrea Vedaldi
- Trevor Darrell
Citations in the corpus
- Deep Joint Image Filtering (2016, methodology): “(a) Input (b) RGF [9] (c) L0 [34] (d) Xu [8] (e) Ours (f) Kopf [33]”
- Fast End-to-End Trainable Guided Filter (2018, background): “For example, image processing techniques enable smooth an image while preserving the edges [39, 41, 43], enhance the details of an image [13, 36], transfer style from a reference image [3, 4],”
- Learning Guided Convolutional Network for Depth Completion (2019, methodology): “Early joint filtering methods [37]–[39] explore the common structures between target and reference images and formulate the problem as iterative energy minimization.”
- Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal (2016, methodology): “(c) Base layer [24] (d) Base layer [25] (e) Base layer [26]”
- Fast Image Processing with Fully-Convolutional Networks (2017, background): “Techniques developed in the last decade can dramatically enhance detail [24, 69, 26, 28, 60], transform the image by applying a master photographer’s style [7, 5], smooth the image for the purpose of abstraction [73, 76, 79], and eliminate the effects of atmospheric scattering [25, 35, 27, 9].”
- A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing (2017, methodology): “We tested 8 image smoothing algorithms for the network to approximate, including bilateral filter (BLF) [26], iterative bilateral filter (IBLF) [8], rolling guidance filter (RGF) [42], RTV texture smoothing ]) Figure 6.”
- Removing Rain from Single Images via a Deep Detail Network (2017, methodology): “The base layer can be obtained using low-pass filtering of X [11, 32, 37] after which the detail layer Xdetail = X−Xbase.”
- Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network (2016, methodology): “First row: Results by approximating RGF [37].”