# Review — Blur Classification Using Wavelet Transform and Feed Forward Neural Network (Blur Classification)

## Blur Classification Using Wavelet + Neural Network

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

**Preprocessing****Feature Extraction Using Wavelet****Neural Network****Experimental Results**

**1. Preprocessing**

- First, the color image obtained by the digital camera is converted into an
**8-bit grayscale image**. - The
**2D Hanning window**gives a fine trade-off between forming a smooth transition towards the image borders and maintaining enough image information in power spectrum. - A centered portion of
**size****128×128 is cropped**.

# 2. Feature Extraction Using Wavelet

**Matlab Wavelet transforms**toolbox used for wavelet transform with**decomposition level 3**.- The
**Haar wavelet**is chosen the Haar wavelet is theoretically straightforward and precisely reversible without edge effects. - The Haar transform does not have overlapping windows, which reflects only changes between adjacent pairs of pixels.
- The energy of an approximation image, i.e.
**LL, is generally not considered as a feature.** - The
**mean**and**standard deviation**of the coefficients related with**each decomposition**is calculated. - The
**mean**of a detail image*Ii*is calculated as:

- where
*M*×*N*is the size of detail image and*Ei***energy of detail image**and Energy*Ei*is calculated by the**sum of absolute values of wavelet coefficients:**

- The standard deviation of a detail image is calculated as:

- So,
**a feature vector of size 18**is obtained, which consists of**9 mean****energies**and**9 standard deviations**:

**3. Neural Network**

- 350 images from a QR code image database [23] is used.
- The three different classes of blur i.e. motion, defocus and joint blur were synthetically introduced with different parameters to make the databases of 1050 blurred images (i.e., 350 images for each class of blur).
- A three layer Neural network was created with 18-nodes in the first (input) layer corresponding to size of input feature vector. 1 to 50 nodes in the hidden layer, and 3 nodes in the output layer (i.e. one node for each class).
- The whole training and testing features set is normalized into the range of [0, 1].
- To perform the cross-validation procedure whole feature set is divided randomly into 3 sets training set, validation set and test set with a ratio 0.5, 0.2 and 0.3 respectively.
- Finally,
**10 nodes in the hidden layer were selected**to run the final simulation.

# 4. Experimental Results

- The values of positive and Negative samples used as testing samples are 350 and 700, respectively for each blur categories.
- Testing results give classifications
**accuracies**as**99.3%, 99.7%, and 100%**for motion, defocus and joint blur categories respectively. - These classification accuracies show the
**high precision**of the proposed method.

Later, Tiwari IJEEI’17 outperforms the method in this story.

## Reference

[2014 IJMECS] [Tiwari IJMECS’14]

Blur Classification Using Wavelet Transform and Feed Forward Neural Network

## Blur Classification

**2014** [Tiwari IJMECS’14] **2017** [Tiwari IJEEI’17] [SFA] **2019 **[SFA & SFGN] **2020** [Gueraichi SAI’20] [Szandała SSCI’20]** **[Tiwari IJISMD’20]