In this story, DevNet: Deep Event Network for Multimedia Event Detection and Evidence Recounting, (DevNet), by Tsinghua University, Hong Kong University of Science and Technology, University of Technology, Sydney, and Carnegie Mellon University, is reviewed.

An event is a semantic abstraction of video sequences of higher level than a concept and often consists of multiple concepts.

A long unconstrained video may contain a lot of irrelevant information and even the same event label may contain large intra-class variations.

In this paper:

• Deep Event Network (DevNet), is designed to simultaneously detect pre-defined events and provide key spatial-temporal evidences.
• A spatial-temporal saliency…

In this story, Blur Classification Using Wavelet Transform and Feed Forward Neural Network, (Tiwari IJMECS’14), by Mody Institute of Technology & Science, is briefly reviewed. In this paper:

• Features are extracted in wavelet domain and are fed into a feed forward neural network for blur classification.

This is a paper in 2014 IJMECS. (Sik-Ho Tsang @ Medium)

Outline

1. Preprocessing
2. Feature Extraction Using Wavelet
3. Neural Network
4. Experimental Results

1. Preprocessing

In this story, Comparative Study of Classifiers for Blurred Images, (Gueraichi SAI’20), by Houari Boumediene University of Science and Technology, is reviewed. In this paper:

• A robust learning model based on the transformation into DCT is built, for the classification of images according to their degree of blur.
• Naïve Bayes, MLP, k-NN, SVM, RF are tried. Finally, Random Forest (RF) is found to be the best.

This is a paper in 2020 SAI. (Sik-Ho Tsang @ Medium)

Outline

1. Features Extraction Using DCT
2. Experimental Results

1. Features Extraction Using DCT

• An image of the Gblur dataset (from Live database) is divided into blocks of size (8×8) and…

In this story, A Pattern Classification Based Approach for Blur Classification, (Tiwari IJEEI’17), by Mody University of Science & Technology, is reviewed. In this paper:

• Curvelet transform based energy features are utilized as features of blur patterns and a neural network is designed for blur classification: motion, defocus, and combined blur.

This is a paper in 2017 IJEEI. (Sik-Ho Tsang @ Medium)

Outline

1. Pre-Processing
2. Feature Extraction Using Curvelet Transform
3. Neural Network
4. Experimental Results

1. Pre-processing

• First, a color image is converted into an 8-bit grayscale image.
• Then, Hanning window is applied to the image.
• The windowed image is transformed to the frequency domain…

In this story, A Blur Classification Approach Using Deep Convolution Neural Network, (Tiwari IJISMD’20), by University of Petroleum and Energy Studies, is reviewed. In this paper:

• Four blur categories: motion, defocus, Gaussian, and box blur are classified for hand gesture images by using CNN.
• It is essential to determine the exact blur type for blind image restoration.

This is a paper in 2020 IJISMD. (Sik-Ho Tsang @ Medium)

Outline

1. Blur Models
2. Blur Classification Framework
3. Experimental Results

1. Blur Models

1.1. Motion Blur

• The motion blur appears when the object to be captured moves relative to the imaging device:

In this story, Convolutional Neural Network for Blur Images Detection as an Alternative for Laplacian Method,(Szandała SSCI’20), by Wroclaw University of Science and Technology, is briefly reviewed, as I’m studying this problem recently.

The photographers employed for picturing ceremonies such as weddings admit that as many as 40% of the images have insufficient quality to be proposed to a client. One of the key factors that lead to quality degradation is blur.

• In this paper, a CNN is designed to classify if an image is blurred or not.

This is a paper in 2020 SSCI. (Sik-Ho Tsang @ Medium)

Outline

1. Network…

In this story, Blur image identification with ensemble convolution neural networks, (SFA & SFGN), by Beihang University and University of Connecticut, is reviewed.

Blur image type classification is essential to blur image recovery.

In this paper:

• Simplified-Fast-AlexNet (SFA) and Simplified-Fast-GoogleNet (SFGN), are designed.
• Ensemble of SFA and SFGN is used for blur classification (Gaussian blur, motion blur, defocus blur and haze blur).

This is a paper in 2019 JPR with high impact factor of 4.384. This paper is an extension of SFA in 2017 IST. (Sik-Ho Tsang @ Medium)

Outline

1. Simplified-Fast-AlexNet (SFA): Network Architecture
2. Simplified-Fast-GoogleNet (SFGN): Network Architecture
3. Ensemble of SFA…

In this story, Blur Image Classification based on Deep Learning, (SFA), is reviewed. In this paper:

• Simplified-Fast-AlexNet (SFA) is designed to classify if an image is blurred by defocus blur, Gaussian blur, haze blur, or motion blur.

This is a paper in 2017 IST. (Sik-Ho Tsang @ Medium)

Outline

1. Brief Overview of Image Blur Modelling
2. Simplified-Fast-AlexNet (SFA): Network Architecture
3. Datasets
4. Experimental Results

1. Brief Overview of Image Blur Modelling

• Image blur issue can be regarded as the image degradation process from the high-quality images to the low-quality blurred images: