Super Resolution with CoreML (Long Version)

This is the slide presented at try! Swift NewYork 2018.
https://www.tryswift.co/events/2018/nyc/#coreml

The ‘Super Resolution' technique is used for converting low resolution images into high resolution, which reduces the amount of image data that needs to be transferred. In this talk, I'd like to show you the implementation of super resolution with CoreML and Swift, and compare the results with conventional methods. I’ll also talk about how to train your own model using your own data step by step. In addition, I’d like to introduce recent topics such as Turi Create, Swift for TensorFlow, CoreML2 and CreateML, which will be enhanced in iOS 12 and I’ll look at how iOS will take advantage of machine learning technology in the future.

Transcript

1. Super Resolution with CoreML Kentaro Matsumae Mercari #tryswiftnyc Sep 5th 2018 try! Swift NYC
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72. This is useful for any type of image. Not only manga.
73. This is useful for any type of image. Not only manga. Give it a try!
74. https://github.com/kenmaz/SuperResolutionKit
75. Open Source https://github.com/kenmaz/SuperResolutionKit
76. Easy to use let imageView: UIImageView = … let image: UIImage = … imageView.setSRImage(image) //Super Resolution⭐ pod “SuperResolutionKit”
77. SuperResolutionKit
78. SuperResolutionKit •UIImageView extension
79. SuperResolutionKit •UIImageView extension •SRConverter (UIImage to UIImage)
80. SuperResolutionKit •UIImageView extension •SRConverter (UIImage to UIImage) •Include pre-trained model (FSRCNN, SRCNN)
81. SuperResolutionKit •UIImageView extension •SRConverter (UIImage to UIImage) •Include pre-trained model (FSRCNN, SRCNN) •Include Python script to train your own model
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98. from keras.models import Sequential from keras.layers import Conv2D from keras.optimizers import Adam ch = 1 SRCNN = Sequential(input_shape=(200, 200, ch)) SRCNN.add(Conv2D(128, 9, 9, activation='relu')) SRCNN.add(Conv2D(64, 3, 3, activation='relu')) SRCNN.add(Conv2D(ch, 5, 5, activation='linear')) adam = Adam(lr=0.0003) SRCNN.compile(optimizer=adam, loss='mean_squared_error')
99. let Sequential = Python.import("keras.models.Sequential") let Conv2D = Python.import("keras.layers.Conv2D") let Adam = Python.import("keras.optimizers.Adam") let ch = 1 let SRCNN = Sequential(input_shape: (200, 200, ch)) SRCNN.add(Conv2D(128, (9, 9), activation: 'relu')) SRCNN.add(Conv2D(64, (3, 3), activation: 'relu')) SRCNN.add(Conv2D(ch, (5, 5), activation: 'linear')) let adam = Adam(lr: 0.0003) SRCNN.compile(optimizer: adam, loss: 'mean_squared_error')
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