scholarly journals Single Image Re ection Removal via Deep Feature Contrast

2022 ◽  
Vol 16 ◽  
pp. 311-320
Author(s):  
Lumin Liu
Keyword(s):  
Texture Feature ◽  
Computational Photography ◽  
Single Image ◽  
Low Level ◽  
Level Information ◽  
Fast Development ◽  
Model Training ◽  
Parallel Feature ◽  
Synthetic Datasets ◽  
Background Image

Removing undesired re ection from a single image is in demand for computational photography. Re ection removal methods are gradually effective because of the fast development of deep neural networks. However, current results of re ection removal methods usually leave salient re ection residues due to the challenge of recognizing diverse re ection patterns. In this paper, we present a one-stage re ection removal framework with an end-to-end manner that considers both low-level information correlation and efficient feature separation. Our approach employs the criss-cross attention mechanism to extract low-level features and to efficiently enhance contextual correlation. To thoroughly remove re ection residues in the background image, we punish the similar texture feature by contrasting the parallel feature separa- tion networks, and thus unrelated textures in the background image could be progressively separated during model training. Experiments on both real-world and synthetic datasets manifest our approach can reach the state-of-the-art effect quantitatively and qualitatively.

scholarly journals Artifact-Free Single Image Defogging

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 577
Author(s):  
Gabriele Graffieti ◽  
Davide Maltoni
Keyword(s):  
Learning Strategy ◽  
Real Data ◽  
Training Procedure ◽  
Single Image ◽  
Paired Data ◽  
Manual Inspection ◽  
Synthetic Datasets ◽  
Visibility Enhancement ◽  
Difficult Cases ◽  
Good Contrast

In this paper, we present a novel defogging technique, named CurL-Defog, with the aim of minimizing the insertion of artifacts while maintaining good contrast restoration and visibility enhancement. Many learning-based defogging approaches rely on paired data, where fog is artificially added to clear images; this usually provides good results on mildly fogged images but is not effective for difficult cases. On the other hand, the models trained with real data can produce visually impressive results, but unwanted artifacts are often present. We propose a curriculum learning strategy and an enhanced CycleGAN model to reduce the number of produced artifacts, where both synthetic and real data are used in the training procedure. We also introduce a new metric, called HArD (Hazy Artifact Detector), to numerically quantify the number of artifacts in the defogged images, thus avoiding the tedious and subjective manual inspection of the results. HArD is then combined with other defogging indicators to produce a solid metric that is not deceived by the presence of artifacts. The proposed approach compares favorably with state-of-the-art techniques on both real and synthetic datasets.

scholarly journals Haziness Degree Evaluator: A Knowledge-Driven Approach for Haze Density Estimation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3896
Author(s):  
Dat Ngo ◽  
Gi-Dong Lee ◽  
Bongsoon Kang
Keyword(s):  
Objective Function ◽  
Density Estimation ◽  
Digital Terrain Model ◽  
Single Image ◽  
Image Dehazing ◽  
Terrain Model ◽  
Digital Terrain ◽  
Proposed Model ◽  
Synthetic Datasets ◽  
Computation Analysis

Haze is a term that is widely used in image processing to refer to natural and human-activity-emitted aerosols. It causes light scattering and absorption, which reduce the visibility of captured images. This reduction hinders the proper operation of many photographic and computer-vision applications, such as object recognition/localization. Accordingly, haze removal, which is also known as image dehazing or defogging, is an apposite solution. However, existing dehazing algorithms unconditionally remove haze, even when haze occurs occasionally. Therefore, an approach for haze density estimation is highly demanded. This paper then proposes a model that is known as the haziness degree evaluator to predict haze density from a single image without reference to a corresponding haze-free image, an existing georeferenced digital terrain model, or training on a significant amount of data. The proposed model quantifies haze density by optimizing an objective function comprising three haze-relevant features that result from correlation and computation analysis. This objective function is formulated to maximize the image’s saturation, brightness, and sharpness while minimizing the dark channel. Additionally, this study describes three applications of the proposed model in hazy/haze-free image classification, dehazing performance assessment, and single image dehazing. Extensive experiments on both real and synthetic datasets demonstrate its efficacy in these applications.

scholarly journals A Lightweight Dense Connected Approach with Attention on Single Image Super-Resolution

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1234
Author(s):  
Lei Zha ◽  
Yu Yang ◽  
Zicheng Lai ◽  
Ziwei Zhang ◽  
Juan Wen
Keyword(s):  
Receptive Fields ◽  
Super Resolution ◽  
Feature Representation ◽  
Single Image ◽  
Deep Model ◽  
Benchmark Datasets ◽  
Rich Information ◽  
Image Super Resolution ◽  
Model Training ◽  
Single Image Super Resolution

In recent years, neural networks for single image super-resolution (SISR) have applied more profound and deeper network structures to extract extra image details, which brings difficulties in model training. To deal with deep model training problems, researchers utilize dense skip connections to promote the model’s feature representation ability by reusing deep features of different receptive fields. Benefiting from the dense connection block, SRDensenet has achieved excellent performance in SISR. Despite the fact that the dense connected structure can provide rich information, it will also introduce redundant and useless information. To tackle this problem, in this paper, we propose a Lightweight Dense Connected Approach with Attention for Single Image Super-Resolution (LDCASR), which employs the attention mechanism to extract useful information in channel dimension. Particularly, we propose the recursive dense group (RDG), consisting of Dense Attention Blocks (DABs), which can obtain more significant representations by extracting deep features with the aid of both dense connections and the attention module, making our whole network attach importance to learning more advanced feature information. Additionally, we introduce the group convolution in DABs, which can reduce the number of parameters to 0.6 M. Extensive experiments on benchmark datasets demonstrate the superiority of our proposed method over five chosen SISR methods.

Visualizing Speciation in Artificial Cichlid Fish

2006 ◽  
Vol 12 (2) ◽  
pp. 243-257 ◽  
Author(s):  
Ross Clement
Keyword(s):  
Phylogenetic Tree ◽  
Phylogenetic Trees ◽  
Cichlid Fish ◽  
Natural Consequence ◽  
Open Problems ◽  
Visualization System ◽  
Low Level ◽  
Wide Range ◽  
Level Information ◽  
History Of

The Cichlid Speciation Project (CSP) is an ALife simulation system for investigating open problems in the speciation of African cichlid fish. The CSP can be used to perform a wide range of experiments that show that speciation is a natural consequence of certain biological systems. A visualization system capable of extracting the history of speciation from low-level trace data and creating a phylogenetic tree has been implemented. Unlike previous approaches, this visualization system presents a concrete trace of speciation, rather than a summary of low-level information from which the viewer can make subjective decisions on how speciation progressed. The phylogenetic trees are a more objective visualization of speciation, and enable automated collection and summarization of the results of experiments. The visualization system is used to create a phylogenetic tree from an experiment that models sympatric speciation.

B-Spline based globally optimal segmentation combining low-level and high-level information

2018 ◽  
Vol 73 ◽  
pp. 144-157 ◽  
Author(s):  
Shenhai Zheng ◽  
Bin Fang ◽  
Laquan Li ◽  
Mingqi Gao ◽  
Rui Chen ◽  
...  
Keyword(s):  
B Spline ◽  
Low Level ◽  
Level Information ◽  
Optimal Segmentation ◽  
High Level
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