Review — SFA & SFGN: Simplified-Fast-GoogleNet (Blur Classification)

Blur Classification Using Ensemble of Simplified-Fast-GoogleNet (SFGA) and Simplified-Fast-AlexNet (SFA)

Sample images in blur datasets
  • Ensemble of SFA and SFGN is used for blur classification (Gaussian blur, motion blur, defocus blur and haze blur).


  1. Simplified-Fast-AlexNet (SFA): Network Architecture
  2. Simplified-Fast-GoogleNet (SFGN): Network Architecture
  3. Ensemble of SFA and SFGN
  4. Overall Framework
  5. Datasets
  6. Experimental Results

1. Simplified-Fast-AlexNet (SFA): Network Architecture

Simplfied-Fast-AlexNet (SFA): Network Architecture
  • Except that, the ReLU is changed to Leaky ReLU (LReLU).
  • (If interested, please feel free to read SFA in 2017 IST.)

2. Simplified-Fast-GoogleNet (SFGN): Network Architecture

Simplified-Fast-GoogleNet (SFGN): Network Architecture
  • SFGN is obtained by pruning GoogLeNet, with only the layers till the first loss.
  • The number of neurons are compressed by a ratio of 50%, just like SFA.
  • Batch normalization and LReLU are used.

3. Ensemble of SFA and SFGN

Ensemble of SFA and SFGN
  • The corresponding weights of SFA and SFGN are defined as Weight1 = C1/(C1 + C2) and Weight2 = C2/(C1 + C2), respectively.

4. Overall Framework

Overall Framework
  • The improved SLIC super-pixel segmentation method is used to extract blurred area from the blurred images to form a real blurred image dataset containing only global blurred images.
Improved SLIC
  • The modified SLIC method also considers the blur feature distance.
  • The information entropy and SVD ratio are also considered to select the purely blur image patches.
  • (If interested, please feel free to read the paper directly.)

5. Datasets

5.1. Training Dataset

  • Similar to SFA, Gaussian blur, motion blur and defocus blur are synthesized. But in this paper, haze blur is also synthesized.
  • 200,000 128×128×3 simulated global blur patches are used for training.
  • 62,000 real/natural blur patches are obtained from online website.
  • All four blur types are uniformly distributed.

5.2. Testing Dataset 1

  • Berkeley dataset images and Pascal VOC 2007 dataset are selected to be the testing dataset.
  • In total 21,000 global blur test sample patches are obtained in which 5,560 haze blur image patches possess the same sources with training samples.

5.3. Testing Dataset 2

  • A dataset consisting of 13,810 natural global blur image patches is constructed. The samples are all collected from the same websites as the haze blur samples in Training dataset.

6. Experimental Results

6.1. The Integrated CNN Performance

Comparison of different models under several criteria.

6.2. SOTA Comparison

Comparison of the ensemble classifier and the state-of-the-art.
  • The prediction accuracy ( > 90%) of learned feature-based methods is generally superior to the ones ( < 90%) whose use handcrafted features.

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