The document discusses a deep learning approach for image super-resolution using a Super-Resolution Convolutional Neural Network (SRCNN), which efficiently maps low-resolution images to high-resolution ones with superior accuracy compared to traditional methods. It highlights the network's architecture, training process, and the advantages of a lightweight structure that minimizes additional processing. Experiments show that SRCNN can enhance image restoration quality, with potential for further improvement through filter expansion and network deepening.

1. DEEP LEARNING FOR
IMAGE SUPER-
RESOLUTION
CHAO DONG, CHEN CHANGE LOY,
KAIMING HE, XIAOOU TANG
Presented By Prudhvi Raj Dachapally
D. Prudhvi Raj

2. Abstract
Using Deep Convolutional Networks, the
machine can learn end-to-end mapping between
the low/high-resolution images. Unlike traditional
methods, this method jointly optimizes all the
layers of the image. A light-weight CNN structure
is used, which is simple to implement and
provides formidable trade-off from the existential
methods.

3. What is Deep Learning?
 A branch of Artificial Neural Networks and
Machine Learning that deals with more
convolutional and realistic brain structures.
 In the words of Dr. Andrew Ng, researcher at
Stanford, Founder & CEO of Coursera,
“Increased computing power has allowed us to
map and process much larger neural networks
than ever before.”

4. Appealing Properties of the
Proposed Model
 The name given for this model is Super – Resolution
Convolutional Neural Network (or) SRCNN.
 Structure is simple, but provides superior accuracy
compared to state-of-the-art methods.
 Since it is a fully feed-forward network, it is
unnecessary to solve the optimization problem.
 Restoration quality can be further improved with more
diverse data and/or more deeper network without
changing the core structure of the network.
 SRCNN model can also cope with channels of color
images simultaneously with ease, which in turn can
improve performance.

5. Preliminaries
 Color Channel used – YCbCr
 Y – Luminance
 Cb – Blue – difference
 Cr – Red – difference
 Cb and Cr are Chrominance components
 First, we upscale the image to a desired size
using bicubic interpolation method. This is just
a pre-processing step.

6. Structure of the Network

7. Components in the Network
 Patch Extraction and Representation
 Densely extracts patches and then represents them as a set of filters.
 This layer is expressed as a function F1, where
 F 1(Y) = max(0, W1 * Y + B1)
 This layer extracts a n1 –dimensional feature for each patch.
 Non – Linear Mapping
 Maps each of the n1-dimensional vectors into an n2-dimensional one.
 This layer is expressed as a function F2, where
 F 2(Y) = max(0, W2 * F1(Y) + B2)
 It is possible to add more convolutional layers to this structure, but in
perspective, increases the training time.
 Reconstruction
 The predicted overlapping high-resolution patches are often averaged to
produce the final full image.
 This convolutional layer is defined as
 F (Y) = W3 * F 2(Y) + B3

8. Terms Used in the Formulations
 W1 = Corresponds to the n1 filters of size
c * f1 * f1,
 Where c is number of channels and f1 is the spatial
size of the filter.
 B1 = An n1-dimensional vector, whose each
element is associated with a filter.
 W2 = n2 filters of size n1 * f2 * f2.
 B2 = n2 dimensional vector.
 W3 = Corresponds to c filters of size n2 * f3 * f3
 B3 = c- dimensional vector.

9. Learning Process
 Estimation of network parameters can be
achieved through minimizing the loss between
reconstructed images and the corresponding
original high-resolution images. This is done
by taking the Mean Squared Error (MSE).
 Using MSE as a loss function, favors high
PSNR( Peak Signal to Noise Ratio).
 The loss is minimized by using stochastic
gradient descent with regular back-
propagation algorithm.

10. Experiments
 Training Data
 Very Large Data Set of 395, 909 images from
2013 ImageNet Competition.
 Test Data
 A BSD200 Data Set with 200 images.
 Basic Network Settings
 These are f1 = 9, f2 = 1, f3 = 5, n1 = 64 and n2 =
32.

11. Results

12. Comparison Against the State-of-
the-art Methods

13. Real Time Results

14. Expansion Scope
 Using Large Filters
 Increasing the filter size can increase the PSNR
value, but also increases the training time.
 Using Deeper Networks
 This can sometimes be a contradiction to the rule
“More the layers, so is the accuracy.”

15. Conclusion
 This approach, SRCNN, learns an end-to-end
mapping between low- and high-resolution
images, with little extra pre/post-processing
beyond the optimization. With a lightweight
structure, the SRCNN achieves a superior
performance than the state-of-the-art methods.
Additional improvement in performance can be
gained further by exploring more filters and
different training strategies.

16. References
Images, tables and some of the text used in this
presentation as taken from Chao Dong et.al.
“Image Super-Resolution Using Deep
Convolutional Networks”, IEEE Transactions on
Pattern Analysis and Machine Intelligence,
Volume 38, February 2016.

17. Thank You