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Table of contents
1.
Introduction
2.
About Image Super-resolution
3.
How deep learning creates high-resolution images?
4.
Image Super-Resolution frameworks
4.1.
Pre-upsampling Super-resolution
4.1.1.
SRCNN
4.1.2.
VDSR
4.2.
Post-upsampling Super-resolution 
4.2.1.
FSRCNN
4.2.2.
ESPCN
4.2.3.
Residual networks
5.
Implementation of Image Super-Resolution
6.
Applications of image super-resolution
7.
Frequently Asked Questions
7.1.
Are there more methods to perform image super-resolution?
7.2.
What is the most widely used image super-resolution method?
7.3.
Can we implement image super-resolution methods by using other libraries?
8.
Conclusion
Last Updated: Mar 27, 2024

Image Super-Resolution

Author Md Yawar
0 upvote
Master Python: Predicting weather forecasts
Speaker
Ashwin Goyal
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Introduction

Images are an integral part of our life. It is difficult to imagine our lives without pictures. But sometimes, the images that we take have low resolution. Due to this, we cannot get the details that we want in our image. Applying a sharpen filter may make our image look sharper, but the fine details are lost. Image super-resolution is an attempt to increase the resolution of our image without losing the details. Let us dive deeper into this process and try to understand its working.
 

Source

                                           Low-resolution image                                                                         High-resolution image

About Image Super-resolution

Image super-resolution aims to create a high-resolution image from low-resolution images. It can retrieve fine details of an image that may be not present in the low-resolution image. A high-resolution image can be created from a set of many low-resolution images of the same object or even from a single low-resolution image (Single Image Super-Resolution - SIRS). Image super-resolution is commonly used in many computer vision applications ranging from medical imaging to satellite imaging. It can be performed by using various deep learning techniques. We will talk about different deep learning methods to generate high-resolution images.

Also See, Image Sampling 

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How deep learning creates high-resolution images?

A degradation function can model a low-resolution image from a high-resolution image. This can be understood from the below equation:

                                  Ix = D( Iy; σ)

Where Ix is the low-resolution image, Iy is the high-resolution image, and D and σ are the degradation parameters. D and σ are unknown and are taken randomly at first. Deep learning models predict the value of D and σ and find the inverse of the degradation function. The prediction is made by using the data provided in the form of high-resolution and low-resolution images.

Let us look at some of the techniques used to perform this task.

Image Super-Resolution frameworks

Pre-upsampling Super-resolution

Upsampling simply means increasing the resolution of an image. In the pre-upsampling super-resolution, we use traditional techniques like bipolar interpolation to upscale an image and then use deep learning to refine the upscaled image. Let us talk about some of the 

SRCNN


Super Resolution Convolution Neural Network (SRCNN) is one of the first methods to use deep learning for image super-resolution. SRCNN is a convolutional neural network. It contains three layers. 
Feature extraction layer: It extracts the features from the input and uses different convolutional filters to represent them.
Non-linear mapping: It is used to change the number of channels of the image to perform further processing of the image. It contains 1X1 convolutional filters.
Reconstruction layer: It is the final layer of SRCNN and is used to construct the high-resolution image.

SRCNN

VDSR


VDSR stands for Very deep super-resolution. It is based on VGG architecture and uses many small 3X3 filters. It is an improved version of SRCNN. 
Instead of learning the direct mapping from the input image like SRCNN, VDSR is used to get the residuals of the image, and then the initial input image is added to get the high-resolution output.

SRCNN vs VDSR 

Post-upsampling Super-resolution
 

In pre-upsampling super-resolution, feature extraction is done after upsampling of the image. This requires a lot of computational power. To mitigate this problem, we use post-upsampling Super-resolution. In this, the feature extraction is done before upscaling the image. This helps us to cut down the computational cost significantly. Upscaling is done using complex methods such as sub-pixel convolution. This helps to create an end-to-end trainable model.
 

FSRCNN



                                                                                     FSRCNN VS SRCNN

FSRCNN stands for Fast Super-Resolution Convolutional Neural Network. The feature extraction is done before the upsampling of the image. It uses 5X5 convolutional filters for feature extraction and the 1X1 filters to reduce the size. Multiple 3X3 filters are used for mapping.
The upsampling is done using deconvolution. This improves the model. FSRCNN is faster and more accurate than SRCNN.
 

ESPCN

Efficient Sub-Pixel Convolutional Neural Network (ESPCN). It uses sub-pixel convolution for upsampling. ESPCN converts different channels of a low-resolution image to form a single channeled high-resolution image.

                                                                                                             ESPCN network architecture

Residual networks

Residual networks are also used to perform image super-resolution.

EDSR stands for Enhanced Deep Residual Networks. It contains multiple residual blocks and its architecture is based on SRResNET. To create EDSR, batch normalization layers are removed from SRResNet.

                 source

Implementation of Image Super-Resolution

Let us try to implement image super-resolution using OpenCV. We will use FSRCNN to generate a high-resolution image from a low-resolution image.

Importing the libraries

import cv2
import matplotlib.pyplot as plt

 

Reading the image

img = cv2.imread("path/to/image.png")

 

Using FSRCNN

sr = cv2.dnn_superres.DnnSuperResImpl_create()
path = "FSRCNN_x3.pb"
sr.readModel(path)
sr.setModel("fsrcnn",3)
result = sr.upsample(img)

 

FSRCNN will increase the resolution of the image. The desired output will be like this:

Applications of image super-resolution

  1. It is widely used in the medical field.
  2. Used in remote sensing by enhancing the picture quality.
  3. Increase the quality of the images taken from surveillance.
  4. Astronomical imaging - the images taken from telescopes are enhanced using super-resolution.
  5. Improving facial recognition accuracy.
    Also read, Sampling and Quantization

Frequently Asked Questions

Are there more methods to perform image super-resolution?

Yes, there are many other methods such as Super-Resolution Generative Adversarial Network (SRGAN), MDSR, CARN, etc.

What is the most widely used image super-resolution method?

SRCNN is considered the most widely used super-resolution method.

Can we implement image super-resolution methods by using other libraries?

Yes, we can also use other libraries such as TensorFlow for implementing image super-resolution methods.

Conclusion

This article talked about image super-resolution and the basic idea of achieving it by using deep learning. We also learned about some of the deep learning methods for super-resolution. We talked about its application and implemented FSRCNN by using OpenCV. But the learning should not stop here. There are more methods of image super-resolution. 

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