Flexible Bilevel Image Layer Modeling For Robust Deraining

2020 ◽  
Author(s):  
Jian Chen ◽  
Pan Mu ◽  
Risheng Liu ◽  
Xin Fan ◽  
Zhongxuan Luo
Keyword(s):  
Image Layer

Single Image Layer Separation via Deep Admm Unrolling

2018 ◽  
Author(s):  
Risheng Liu ◽  
Zhiying Jiang ◽  
Xin Fan ◽  
Haojie Li ◽  
Zhongxuan Luo
Keyword(s):  
Single Image ◽  
Layer Separation ◽  
Image Layer

scholarly journals Evaluation of the panoramic image formation in different anatomic positions

2010 ◽  
Vol 21 (5) ◽  
pp. 458-462 ◽  
Author(s):  
Daniela Brait Silva Ladeira ◽  
Adriana Dibo da Cruz ◽  
Solange Maria de Almeida ◽  
Frab Norberto Bóscolo
Keyword(s):  
Horizontal Plane ◽  
Image Formation ◽  
Vertical Axis ◽  
Image Size ◽  
Radiographic Image ◽  
Middle Portion ◽  
Image Layer ◽  
Acrylic Plate ◽  
Recorded Data ◽  
Digital Caliper

The aim of this study was to determine size, shape and position of the image layer by evaluation of the radiographic image formation in different anatomic positions. A customized phantom was made of a rectangular acrylic plate measuring 14 cm² and 0.3 cm thick, with holes spaced 0.5 cm away and arranged in rows and columns. Each column was separately filled with 0.315 cm diameter metal spheres to acquire panoramic radiographs using the Orthopantomograph OP 100 unit. The customized phantom was placed on the mental support of the device, with its top surface kept parallel to the horizontal plane, and was radiographed at three different heights from the horizontal plane, i.e., the orbital, occlusal and mandibular symphysis levels. The images of the spheres were measured using a digital caliper to locate the image layer. The recorded data were analyzed statistically by the Student'-t test, ANOVA and Tukey' test (?=0.05). When the image size of spheres in horizontal and vertical axes were compared, statistically significant differences (p<0.05) were observed in all areas, portions of the image layer and heights of horizontal plane evaluated. In the middle portion of the image layer, differences in the image size of spheres were observed only along the horizontal axis (p<0.05), whereas no differences were observed along the vertical axis (p>0.05). The methodology used in this determined the precise size, shape and position of the image layer and differences in magnification were observed in both the horizontal and vertical axes.

TRAVELOGUE ENRICHING AND SCENIC SPOT OVERVIEW BASED ON TEXTUAL AND VISUAL TOPIC MODELS

2011 ◽  
Vol 25 (03) ◽  
pp. 373-390 ◽  
Author(s):  
YANWEI PANG ◽  
XIN LU ◽  
YUAN YUAN ◽  
XUELONG LI
Keyword(s):  
Visual Information ◽  
Probabilistic Models ◽  
Layer Model ◽  
Comprehensive Overview ◽  
Image Layer ◽  
Scenic Spot ◽  
Candidate Image ◽  
Image Set ◽  
Web Images ◽  
Historical Sites

We consider the problem of enriching the travelogue associated with a small number (even one) of images with more web images. Images associated with the travelogue always consist of the content and the style of textual information. Relying on this assumption, in this paper, we present a framework of travelogue enriching, exploiting both textual and visual information generated by different users. The framework aims to select the most relevant images from automatically collected candidate image set to enrich the given travelogue, and form a comprehensive overview of the scenic spot. To do these, we propose to build two-layer probabilistic models, i.e. a text-layer model and image-layer models, on offline collected travelogues and images. Each topic (e.g. Sea, Mountain, Historical Sites) in the text-layer model is followed by an image-layer model with sub-topics learnt (e.g. the topic of sea is with the sub-topic like beach, tree, sunrise and sunset). Based on the model, we develop strategies to enrich travelogues in the following steps: (1) remove noisy names of scenic spots from travelogues; (2) generate queries to automatically gather candidate image set; (3) select images to enrich the travelogue; and (4) choose images to portray the visual content of a scenic spot. Experimental results on Chinese travelogues demonstrate the potential of the proposed approach on tasks of travelogue enrichment and the corresponding scenic spot illustration.

scholarly journals Separate in Latent Space: Unsupervised Single Image Layer Separation

2020 ◽  
Vol 34 (07) ◽  
pp. 11661-11668 ◽  
Author(s):  
Yunfei Liu ◽  
Feng Lu
Keyword(s):  
Real World ◽  
Ground Truth ◽  
Single Image ◽  
Ground Truth Data ◽  
Layer Separation ◽  
World Vision ◽  
Image Layer ◽  
Latent Space ◽  
Consistency Constraints ◽  
Ill Posed

Many real world vision tasks, such as reflection removal from a transparent surface and intrinsic image decomposition, can be modeled as single image layer separation. However, this problem is highly ill-posed, requiring accurately aligned and hard to collect triplet data to train the CNN models. To address this problem, this paper proposes an unsupervised method that requires no ground truth data triplet in training. At the core of the method are two assumptions about data distributions in the latent spaces of different layers, based on which a novel unsupervised layer separation pipeline can be derived. Then the method can be constructed based on the GANs framework with self-supervision and cycle consistency constraints, etc. Experimental results demonstrate its successfulness in outperforming existing unsupervised methods in both synthetic and real world tasks. The method also shows its ability to solve a more challenging multi-layer separation task.

scholarly journals Dual-Purpose Method for Underwater and Low-Light Image Enhancement via Image Layer Separation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 178685-178698 ◽  
Author(s):  
Chenggang Dai ◽  
Mingxing Lin ◽  
Jingkun Wang ◽  
Xiao Hu
Keyword(s):  
Image Enhancement ◽  
Low Light ◽  
Dual Purpose ◽  
Layer Separation ◽  
Light Image ◽  
Image Layer
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