Image Decomposition into Cartoon and Texture

2015 ◽  
pp. 117-132
Keyword(s):  
Image Decomposition

A Multiscale Variational Model for Image Decomposition

2014 ◽  
Vol 35 (5) ◽  
pp. 1190-1195
Author(s):  
Jian Bai ◽  
Xiang-chu Feng ◽  
Xu-dong Wang
Keyword(s):  
Image Decomposition ◽  
Variational Model

Scalable image decomposition

2021 ◽  
Author(s):  
Hwanbok Mun ◽  
Gang-Joon Yoon ◽  
Jinjoo Song ◽  
Sang Min Yoon
Keyword(s):  
Image Decomposition

Single nighttime image dehazing based on image decomposition

2021 ◽  
Vol 183 ◽  
pp. 107986
Author(s):  
Yun Liu ◽  
Anzhi Wang ◽  
Hao Zhou ◽  
Pengfei Jia
Keyword(s):  
Image Decomposition ◽  
Image Dehazing ◽  
Nighttime Image

scholarly journals ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

2021 ◽  
Author(s):  
Anil S. Baslamisli ◽  
Partha Das ◽  
Hoang-An Le ◽  
Sezer Karaoglu ◽  
Theo Gevers
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
State Of The Art ◽  
Image Decomposition ◽  
Natural Environments ◽  
Decomposition Algorithms ◽  
Ambient Light ◽  
Fine Grained ◽  
Large Scale Dataset ◽  
Direct Illumination

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

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