# Review — A Pattern Classification Based Approach for Blur Classification (Blur Classification)

## Blur Classification Using **Curvelet Transform + Neural Network**

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

**Pre-Processing****Feature Extraction Using Curvelet Transform****Neural Network****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 using
**Fourier transform**. **A centered portion of size 256×256 is cropped**to perform feature extraction.

# 2. Feature Extraction Using Curvelet Transform

**Wrapping based discrete curvelet transform**using**Curvelab-2.1.2**is applied to a power spectrum of barcode image to obtain its coefficients.- These coefficients are then used to form
**the features of blur patterns.** - After achieving the curvelet coefficients,
**the mean and standard deviation of the coefficients related with each subband**is calculated at the coarsest and the finest scale independently. - The
**mean**of a subband at scale*j*and orientation*l*is calculated as:

- where
*M*×*N**E*(*j*,*l*) is the energy of curvelet transformed image respectively at scale*j*and orientation*l*. - Energy is calculated by the sum of absolute values of curvelet coefficients:

- The above figure has 3-scale, and for each scale, it has 1, 8, 16 wedgelets to represent the orientation.
- (For more details about curvelet transform, please visit the paper of this story, or this paper: Multiresolution Analysis Using Wavelet, Ridgelet, and Curvelet Transforms for Medical Image Segmentation.)
- The
**standard deviation**of a subband at can be shown as:

- In this paper, the above wrapping based discrete curvelet transform, with
**(1, 16, 32)-Orientation at 3-Scale**, is used. **Only first half of the total subbands at a scale are considered**for feature calculation, since the remaining subbands at another side have similar coefficients.- Thus,
**(1+8+16) = 25 subbands**of curvelet coefficients are selected for**calculation of mean and standard deviation.** - Finally, a
**feature vector**with**length of 50**is obtained. The**standard deviations**remain in the**first half**of the feature vector and the**means**are arranged into the**second half**of the feature vector.

# 3. Neural Network

- The whole training and testing features set is normalized into the range of [0, 1].
**Hyperbolic tangent sigmoid functions**are used as the activation function.- The final architecture is selected with
**single hidden layer of fifty neurons**which gives best performance. (Not much details on this.)

# 4. Experimental Results

**Two different barcode image databases**are considered.- The first database WWU Muenster Barcode Database [20] consisting of 1D barcode images and the second one is the Brno Institute of Technology QR code image database [21] captured by digital camera.
- The data is divided into
**training, validation and test sets**in a ratio of**50:20:30**respectively.

## 4.1. 1D Barcode Database

- First 200 images from the 1D barcode database are considered.
- Then,
**the three different classes of blur (motion, defocus and combined blur) were synthetically introduced**with different parameters to make the database of**600 blurred images**(i.e., 200 images with each class of blur) for each type of barcode images.

- The best validation performance is 0.0115, at epoch 39, as shown above.

- The overall classification accuracy is
**98.2%**.

**Blurred and noisy 1D images**are also tested.- The overall classification accuracy is
**94.2%.**

## 4.2. QR Code Database

- First 200 images from the QR code database are considered.
- The same procedure is followed to produce 600 blurred images.

- The overall classification accuracy is achieved as
**98.7%**.

**Blurred and noisy QR code images**are also tested.- The overall classification accuracy is
**96.3%**.

## 4.3. SOTA Comparison

- The proposed method in this paper
**outperforms wavelet transform plus feed forward neural network (****Tiwari IJMECS’14****) [12]**.

## Reference

[2017 IJEEI] [Tiwari IJEEI’17]

A Pattern Classification Based Approach for Blur Classification

## Blur Classification

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