A local metric for defocus blur detection cnn feature learning screenshots

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A Local Metric for Defocus Blur Detection Basedon CNN Feature
Learning
In this paper author is describing concept to defocus (defocus means the area
of the image which has no focus of the camera while capturing will contain
blur) blur from the images, this defocus will restrain humans to understand
important scenes from the images clearly. To remove blur part various
technologies has been introduced such as Sparse Matrix, Singular Matrix
Decomposition etc. All this existing techniques require heavy computation and
take more execution time to remove blur from images.
To overcome from this problem author has introduce new technique with
machine learning algorithm called CNN (convolution neural networks)
algorithm with local metric map. First CNN algorithm will be trained with
publicly available blur images dataset and then CNN kernel layer will be used
to detect blur from images, upon blur detection local metric map using
principal component analysis algorithm will analyse each pixels of the image,
pixels with high intensity (RGB colour value) contains no blur part and pixels
with less intensity will contains blur part. So by applying local metric map each
pixel will be analyse to detect intensity and based on intensity pixels will be
kept or remove from the image. High intensity pixels only will be added to
image and less intensity pixels will removed out. After adding pixels image will
be converted to grey format and then the resultant image will contains only
clean image without blur.
All patches with high intensity pixels will be greater than blur threshold.
Algorithm given in paper will evaluate pixels to detect and optimize blur part.
To train CNN model author has used BLUR IMAGES DATASET and we are also
using same dataset to train CNN model. All blur and sharp images are saved
inside ‘train’ model.
All train images are available inside train folder and test images are available
inside test folder.
CNN working procedure
To demonstrate how to build a convolutional neural network based image
classifier, we shall build a 6 layer neural network that will identify and separate
one image from other. This network that we shall build is a very small network

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that we can run on a CPU as well. Traditional neural networks that are very
good at doing image classification have many more parameters and take a lot of
time if trained on normal CPU. However, our objective is to show how to build
a real-world convolutional neural network using TENSORFLOW.
Neural Networks are essentially mathematical models to solve an optimization
problem. They are made of neurons, the basic computation unit of neural
networks. A neuron takes an input (say x), do some computation on it (say:
multiply it with a variable w and adds another variable b) to produce a value
(say; z= wx+b). This value is passed to a non-linear function called activation
function (f) to produce the final output(activation) of a neuron. There are many
kinds of activation functions. One of the popular activation function is Sigmoid.
The neuron which uses sigmoid function as an activation function will be called
sigmoid neuron. Depending on the activation functions, neurons are named and
there are many kinds of them like RELU, TanH.
If you stack neurons in a single line, it’s called a layer; which is the next
building block of neural networks. See below image with layers
To predict image class multiple layers operate on each other to get best match
layer and this process continues till no more improvement left.
Screen shots
To run this project double click on ‘run.bat’ file to get below screen

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In above screen click on ‘Browse’ button and upload train folder for training
CNN Model
In above screen I am uploading ‘train’ folder for training CNN model, after
uploading folder will get below screen

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In above screen dataset textfield train folder path loaded. Now click on
‘Generate CNN Model Using Blur Images Dataset’ button to generate CNN
model
In above screen we can see CNN model generated and we can see all CNN
details in below black console

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In above screen we can see CONV2D generate multiple kernel layer with
different image sizes to make prediction accurately. First layer image size is 126
and 126 and next layer with 63 and 63 and goes on. Now CNN model generated
and we can upload any test image and detect and remove blur.
Now click on ‘Upload Test Image’ button and upload test image
In above screen from test folder I am uploading ‘1.jpg’ and this is a clean image
without blur and now we can see result from our application by clicking on
‘Run Local Metric Blur Detection Algorithm’ button

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In above screen in uploaded image there is no blur and application also
displaying same message in red colour on the image. Now we will upload
another blur image and test
In above screen I am uploading ‘test2.jpg’ image which contains blur and we
can see below detection results

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In above screen first image is the original image which has some blur and this
blur part is detected by CNN and inform to application and then application
uses propose technique to remove blur from background of second image and
then third image remove all background blur and make it clear. By seeing 3rd
image anybody can understand about the scene in the image easily.
Similarly you can upload other test images and get result

A local metric for defocus blur detection cnn feature learning screenshots

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