This document presents a blur classification approach using a Convolution Neural Network (CNN). It discusses types of image degradation including blur, different blur models, and prior work on blur classification using features and neural networks. The proposed method uses a CNN to classify images into four blur categories (motion, defocus, box, and Gaussian blur) based on the images' frequency spectra. The method is evaluated on a dataset with over 2800 synthetically blurred images from 24 people performing 10 gestures. The CNN achieves an average accuracy of 97% for blur classification, outperforming alternatives using multilayer perceptrons or handcrafted features.

1. A Blur Classification Approach using
Convolution Neural Network
Shamik Tiwari
Department of Virtualization, School of Computer Science
University of Petroleum and Energy Studies, Dehradun, India
shamik.tiwari@ddn.upes.ac.in

2. Contents
• Introduction
• Literature Review
• Convolution Neural Network
• Results
• Conclusion

3. Image Degradation
Images are usually prone to degradation due to a number of reasons such as
• Noise
• Focus is not adjusted
• The camera is moved during image acquisition cause ‘Motion blur'
• A dirty lens
• Low resolution of the images
• Poor illuminance etc.

4. ( ) ( ) ( ) ( )g x, y = h x, y f x, y + x, y
( ) ( ) ( ) ( )G u,v = H u,v F u,v + N u,v
Image Degradation/Restoration Model
6
Spatial domain:
Frequency domain:
),(ˆ yxf

5. Image Blur
Blurring degrades of image quality by reducing the high frequency
components of an image.
Few common blur models are:
 Motion Blur
 Defocus Blur
 Gaussian Blur
 Box Blur
7

6. Image Restoration
The objective of restoration is to obtain an estimate the original image f(x,y)

7. Why image restoration is important?

8. Image Restoration
9
• Non-blind Restoration
Given: Degraded image g(x,y) and blurring function h(x,y)
Design: Restoration scheme such that the distortion between f(x,y) and is
minimized
• Blind Restoration
Given: Degraded image g(x,y)
Design: (i) Estimate blurring function h(x,y)
(ii)Restoration scheme such that the distortion between f(x,y) and is minimized
),(ˆ yxf
),(ˆ yxf
Chapter 2

9. Blur Models
 Motion Blur
When the scene to be recorded translates relative to the camera, the
uniform motion blur can be described as
 Defocus blur
It is caused by a system with circular aperture can be modeled as a
uniform disk with radius R
8
Chapter 2

10. • Gaussian Blur
Blur function are usually approximated by the Gaussian function for a large
range of devices like optical image camera, microscope, telescope, etc.
• Box Blur
Box blur is a mean filter where each pixel in the output image has mean of
its neighbouring pixels defined in some specific region in the input image.
𝑔 𝑥, 𝑦 =
1
𝑚 ∗ 𝑛
෍
𝑥,𝑦∈𝑆 𝑢𝑣
𝑓(𝑥, 𝑦)
ℎ 𝐺 =
1
2𝜋𝜎2
𝒆
−
𝒙2+𝒚2
2𝝈2

11. Blur Type Recognition in Spatial Domain?
Figure: A Sharp Image and blurred versions with three types of blurs

12. Blur Patterns in Frequency Domain

13. Literature Review
Bolan et al. [16] offered a blur image classification and blurred region
recognition technique.this work considers only two classes of blur.
Tiwari et al. [18] blur classification using statistical texture features and
neural network classification into motion, defocus and combined blur.
Tiwari et al. [19] blur classification using wavelet features and neural
network classification into motion, defocus and combined blur.
Tiwari et al. [20] blur classification using Ridgelet texture features and
neural network classification into motion, defocus and combined blur.
Tiwari et al. [21] blur classification using Curvelet features and neural
network classification into motion, defocus and combined blur.

14. Blur Classification Framework

15. Image dataset
The proposed method is evaluated using Triesch gesture database [33].
This database consists of 720 images taken by 24 different persons in
3 dissimilar backgrounds comprise of 10 hand gestures for each. All the
images are synthetically blurred to create the database. All the images
are blurred separately using each class of blur namely motion, defocus,
box and Gaussian blur. Therefore, the blurred image database has the
size 2880 images.

16. Experiment 1: Multilayer Perceptron for Blur
Classification

17. Results

18. Experiment 2: Convolution Neural Network for
Blur Classification

19. Result Analysis
The average accuracies achieved through MLP and CNN models are 93.0 and 97.0
respectively. The synthesis database is also tested with Curvelet transform based energy
features and a feed forward neural network based classification model (Tiwari et al., 2014).
The average accuracy using this model is 95.7.

20. Conclusion and Future scope
This paper have presented two different classification models namely
multi-layer perceptron and convolution neural network to classify blur
into one of the four categories namely motion, defocus, box and
Gaussian blur. The frequency spectrum of blurred images are
considered as input images for these models since blur features are
easily noticeable in frequency domain. From the experimental results, it
is evident that convolution neural network is most suitable classification
model. This blur classification work can be stretched by introducing
more fine-tuned model to improve accuracies in presence and absence
of noise.

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26. Thanks