Using VGGNet+Conv-LSTM, Outperforms BTBCRL, DeFusionNet & DHDE
In this story, Defocus Blur Detection by Fusing Multiscale Deep Features With Conv-LSTM, MsFEN+MsBEN, by Civil Aviation University of China, is reviewed. In this paper:
- Multiscale convolutional features from same image with different sizes are extracted.
- Conv-LSTMs are used to integrate the fused features gradually from top-to-bottom layers, and to generate multiscale blur estimations.
This is a paper in 2020 IEEE ACCESS where ACCESS is a open access journal with high impact factor of 3.745. (Sik-Ho Tsang @ Medium)
Outline
- Multiscale Feature Extraction Sub-Network (MsFEN)
- Multiscale Blur Detection Sub-Network (MsBEN)
- Experimental Results
1. Multiscale Feature Extraction Sub-Network (MsFEN)
In this story, Automatic Extraction of Blur Regions on a Single Image Based on Semantic Segmentation, BDNet, by Southeast University, and International Joint Laboratory of Information Display and Visualization, is reviewed. In this paper:
- Blur Detection Net, BDNet, is proposed to well integrate global image-level context and cross-layer context information by combining ResNet and Fully Convolutional Network (FCN).
This is a paper in 2020 IEEE ACCESS where ACCESS is a open access journal with high impact factor of 3.745. (Sik-Ho Tsang @ Medium)
Outline
- Datasets
- BDNet: Network Architecture
- Experimental Results
1. Datasets
- There are three datasets used in the experiments called BlurRaw, BlurDB1 and BlurDB2. …
Extension of DeFusionNet (CVPR’19). Outperforms DHDE, DBM, DMENet, BTBNet, and DeFusionNet (CVPR’19)
In this story, DeFusionNET: Defocus Blur Detection via Recurrently Fusing and Refining Discriminative Multi-scale Deep Features, DeFusionNET, by China University of Geosciences, National University of Defense Technology, Peng Cheng Laboratory, University of Wollongong, Southwestern University of Finance and Economics, University of Sydney, and University of Salento, is reviewed.
- This paper is the extension of DefusionNet in CVPR’19. Since it is an extension, I will mainly describe the new stuffs in this paper.
- An enhanced feature fusing and refining module (FFRM) is proposed where Channel Attention Module (CAM) and Feature Adaptation Module (FAM) is used.
- A more challenging new dataset is also proposed. …
U-Net-Like Architecture. Outperforms Park CVPR’17 and DBM
In this story, Defocus and Motion Blur Detection with Deep Contextual Features, Kim JCGF’18, by POSTECH, and DGIST, is reviewed. In this paper:
- A deep encoder-decoder U-Net-like network with long residual skip-connections and multi-scale reconstruction loss to exploit high-level contextual features as well as low-level structural features.
- A synthetic dataset is constructed that consists of complex scenes with both motion and defocus blur.
This is a paper in 2018 JCGF, Computer Graphics Forum. (Sik-Ho Tsang @ Medium)
Outline
- Synthetic Dataset for Blur Classification
- Blur Detection and Classification using DCNN
- Experimental Results
1. Synthetic Dataset for Blur Classification
Outperforms Park CVPR’17 / DHCF / DHDE and BTBNet
In this story, DeFusionNET: Defocus Blur Detection via Recurrently Fusing and Refining Multi-scale Deep Features, DeFusionNET, by China University of Geosciences, Zhejiang Normal University, National University of Defense Technology, and University of Sydney, is reviewed. In this paper:
- A fully convolutional network is used to extract multi-scale deep features. These features from different layers are fused as shallow features and semantic features.
- The feature fusing and refining are carried out in a recurrent manner. Finally, the output of each layer at the last recurrent step is fused to obtain the final defocus blur map.
This is a paper in 2019 CVPR with 20 citations. (Sik-Ho Tsang @…
Using Domain Adaptation, Dataset with Synthetic Blurring is used, Outperforms Park CVPR’17
In this story, Deep Defocus Map Estimation using Domain Adaptation, DMENet, by POSTECH, Sungkyunkwan University, and DGIST, are reviewed. In this paper:
- A novel depth-of-field (DOF) dataset, SYNDOF, is produced where each image is synthetically blurred with a ground-truth depth map.
- As the feature characteristics of images in SYNDOF can differ from those of real defocused photos, domain adaptation is used to transfer the features of real defocused photos into those of synthetically blurred ones.
- DMENet consists of four subnetworks: blur estimation, domain adaptation, content preservation, and sharpness calibration networks. The subnetworks are connected to each other and jointly trained.
Ensemble Network With Enhancing Diversity, Outperforms BTBNet & Park CVPR’17 / DHCF
In this story, Enhancing Diversity of Defocus Blur Detectors via Cross-Ensemble Network, CENet, by Dalian University of Technology, is reviewed. In this paper:
- A novel learning strategy by breaking DBD problem into multiple smaller defocus blur detectors and thus estimate errors can cancel out each other.
- Cross-negative and self-negative correlations and an error function are designed to enhance ensemble diversity and balance individual accuracy.
This is a paper in 2019 CVPR with over 15 citations. (Sik-Ho Tsang @ Medium)
Outline
- Problem Formulation
- Single Detector Network (SENet)
- Multi-detector Ensemble Network (MENet)
- Proposed Cross-Ensemble Network (CENet)
- CENet: Network Architecture & Training & Testing
- Experimental…
In this story, Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks, Grad-CAM++, by Indian Institute of Technology Hyderabad, and Cisco Systems, is presented. In this paper:
- Grad-CAM++, built on Grad-CAM, provides better visual explanations of CNN model predictions, in terms of better object localization as well as explaining occurrences of multiple object instances in a single image.
- A weighted combination of the positive partial derivatives of the last convolutional layer feature maps with respect to a specific class score as weights is used to generate a visual explanation for the corresponding class label.
Using a Pyramid M-Shaped Deep Neural Network, Outperforms Park CVPR’17 & Zeng TIP’19, etc.
In this story, Accurate and Fast Blur Detection Using a Pyramid M-Shaped Deep Neural Network, PM-Net, by University of Science and Technology of China, and Shijiazhuang Tiedao University, is reviewed.
There are challenging scenarios using handcrafted features:
- Handcrafted features can hardly differentiate a blurred region from a sharp but homogeneous region.
- Another challenge is that blur metrics based on handcrafted features are difficult to detect the pseudo-sharp backgrounds.
In this paper:
- A novel multi-input multi-loss encoder-decoder network (M-shaped) is proposed to learn rich hierarchical representations related to blur.
- Blur degree is susceptible to scales, a pyramid ensemble model (PM-Net) consisting of different scales of M-shaped subnets and a unifi ed fusion layer, is constructed. …
CNN + PCA for Feature Learning, Iterative Refinement Using Tanh
In this story, A Local Metric for Defocus Blur Detection Based on CNN Feature Learning, Zeng TIP’19, Hunan University, and National Engineering Laboratory for Robot Visual Perception and Control Technology, is reviewed. In this paper:
- The ConvNets automatically learn the most locally relevant features.
- By extracting convolution kernels from the trained neural network structures and processing it with principal component analysis (PCA), the local sharpness metric is automatically obtained by reshaping the principal component vector.
- An effective iterative updating mechanism is proposed to refine the defocus blur detection result from coarse to fine by hyperbolic tangent function.
This is a paper in 2019 TIP where TIP has a high impact factor of 9.34. (Sik-Ho Tsang @…
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