Digital images are prone to various types of degradation such as noise, blur, and compression artifacts due to factors like poor lighting, low-quality camera sensors, and image transmission over the internet. Digital image restoration is the process of improving the quality of a degraded digital image by applying various techniques to remove or reduce the degradation. However, restoring images to their original quality is a complex process that comes with several challenges. Image restoration can be used to remove objects, logos, text, or damaged areas in pictures.

1. 4_5_Image Restoration

2. ● Digital images are prone to various types of degradation such as noise, blur, and
compression artifacts due to factors like poor lighting, low-quality camera sensors,
and image transmission over the internet.
● Digital image restoration is the process of improving the quality of a degraded digital
image by applying various techniques to remove or reduce the degradation.
However, restoring images to their original quality is a complex process that comes
with several challenges.
● Image restoration can be used to remove objects, logos, text, or damaged areas in
pictures.

3. ● Image restoration - 2 phases
● Image reconstruction
○ Fixing damaged images
○ Text removing
○ Logo removing
○ Object removing
● Inpainting
○ Reconstructing lost parts of images
○ Looking at the non-damaged regions

4. Image Restoration Techniques
1. Filtering
2. Image Deblurring
3. Deconvolution
4. Denoising
5. Inpainting
6. Super-resolution
7. Machine learning

5. 1. Filtering
● Images can be restored using filters.
● Filters are used to remove unwanted noise and blur.
● Filtering is used to improve the quality of images that have been degraded due to
factors like poor lighting, camera shakes or low quality equipment.
● The type of filters used for operation of noisy images and estimating the clean and
original image are called restoration filters.
● Restoration processes use blurring and inverse blurring.

6. ● Types of restoration filters.
○ Median Filter
○ Inverse Filter
○ Pseudo Inverse Filter
○ Wiener Filter
○ Gaussian Filter

7. Types of Filters
● Inverse Filter
○ Inverse Filtering is the process of receiving the input of a system from its output.
○ It is the simplest approach to restore the original image once the degradation
function is known.
● Pseudo Inverse Filter
○ Pseudo inverse filter is the modified version of the inverse filter and stabilized
inverse filter.
○ Pseudo inverse filtering gives more better result than inverse filtering but both
inverse and pseudo inverse are sensitive to noise.

8. ● Wiener Filter (Minimum Mean Square Error Filter)
○ Wiener filter executes and optimal trade off between filtering and noise smoothing.
○ It removes the additional noise and inputs in the blurring simultaneously.
○ Wiener filter is real and even.
○ It minimizes the overall mean square error.
● Median Filter
○ Median filter replaces each pixel with the median value of its neighboring pixels to
remove unwanted noise.

9. ● Gaussian Filter
○ Gaussian filters, on the other hand, smooth the image and reduce noise by convolving
the image with a Gaussian kernel.

10. ● Drawbacks of Restoration Filters
○ Not effective when images are restored for the human eye.
○ Cannot handle the common cause of non-stationary signals and noise.
○ Cannot handle spatially variant blurring point spread function.

11. 2. Image Deblurring
● Image deblurring is the process of removing blur from an image that is caused by
factors such as camera shake, motion, defocus, or atmospheric turbulence.
● Blur can degrade the details and clarity of an image, making it difficult to recognize
or analyze.
● Image deblurring aims to recover the original image from the blurred one, using
mathematical models and algorithms.

12. Image Deblurring Techniques
● Blind deconvolution attempts to estimate both the original image and the blur kernel
without any prior information, which can be a challenging and ill-posed problem.
● Non-blind deconvolution assumes that the blur kernel is known or can be estimated from
some additional information.
● Multi-image deblurring uses multiple images of the same scene that are blurred
differently to estimate the blur kernel and original image more accurately.
● Inverse filtering, Wiener filtering, Richardson-Lucy algorithm, iterative methods, deep
learning models, joint optimization, and fusion methods are all techniques used in image
deblurring.

13. 3. Deconvolution
● Deconvolution is another powerful technique used in image processing to remove blur from
an image.
● It is often used in situations where an image has been degraded by a known point spread
function.
● Essentially, deconvolution works by reversing the convolution process that caused the blur in
the first place. This can be thought of as a kind of “unblurring” of the image.
● By removing the blur, important details and features of the image that were previously
obscured can be revealed.
● Deconvolution has a wide range of applications in fields such as astronomy, microscopy, and
medical imaging, and is an important tool for researchers and professionals working in these
areas.

14. 4. Denoising
● Denoising is a common technique used to reduce random noise, such as Gaussian
noise, salt-and-pepper noise, or speckle noise, which can be introduced by various
factors.
● Filters like median filter, bilateral filter, or non-local means filter, or deep learning
models like autoencoders or generative adversarial networks may be employed for
this purpose.

15. 5. Inpainting
● Inpainting is another technique used to fill in missing or damaged parts of an image, such
as scratches, dust, or occlusions.
● This can be done through interpolation, diffusion, or patch-based methods, or deep
learning models like convolutional neural networks or generative adversarial networks.
● Inpainting is the process of reconstructing lost or deteriorated parts of images and
videos.
● Some of the pixels have been replaced by 1s using a binary mask, on purpose, to simulate
a damaged image. Replacing pixels with 1s turns them totally black.
● The mask is a black and white image with patches that have the position of the image bits

16. 6. Super-resolution
● Super-resolution is used to increase the resolution or quality of an image such as
low-resolution, pixelated, or blurred images.
● This can be done through interpolation, reconstruction, or learning-based methods,
or deep learning models like convolutional neural networks or generative
adversarial networks.

17. 7. Machine Learning
● Machine learning is a subfield of artificial intelligence that allows machines to learn from
data and improve their performance over time.
● In the case of image restoration, machine learning algorithms are trained on a dataset of
degraded images and their corresponding restored images, enabling them to learn
patterns and relationships that can be used to restore new images.
● This technique has been shown to produce impressive results, and has been adopted by
many researchers and practitioners in the field of image processing.
● By leveraging the power of machine learning, image restoration can now be
accomplished with greater accuracy and efficiency than ever before.

18. Challenges in Image Restoration

19. 1. Lack of Information
● Sometimes, the degradation in the image is so severe that it is impossible to restore
the image to its original quality.
● This can happen when there is a lack of information in the original image.
● For example, if an image is heavily compressed, some information may be lost,
making it impossible to restore the image to its original quality.

20. 2. Computational Complexity
● Restoring high resolution images requires a lot of processing power.
● Researchers are constantly exploring new techniques and methods to enhance the
accuracy and quality of restored images, while also reducing the computational
demands of the process.
● Despite these advancements, there is still much to learn and improve upon in the
field of image restoration, as it remains a critical area of research in numerous
industries, including healthcare, entertainment, and security.

21. 3. Overfitting
● When it comes to image restoration, overfitting can be a particularly significant
problem.
● In this scenario, the algorithm is trained on a set of images, and it learns to restore
them to their original state.
● However, if the algorithm becomes too specialized during training, it may not be
able to generalize well to new images that it has not seen before.
● This can lead to poor results, which is something that needs to be carefully
considered when using machine learning for image restoration.

23. Python Tools for Image Restoration
● OpenCV, Scikit-image, and TensorFlow are some of the most widely used libraries
for image deblurring and restoration in Python.
● Steps: Import the libraries, load and preprocess the images, apply the desired
methods, save or display the results.

24. ● Import the required libraries
import cv2
import numpy as np
● Load the blurred image
blurred_image = cv2.imread('blur2.jpeg')

25. ● Define the blur kernel size (it's often necessary to experiment with this value)
kernel_size = (15, 15, 3)
● Initialize the blur kernel with a Gaussian kernel
blur_kernel = cv2.getGaussianKernel(kernel_size[0], 1)
blur_kernel = np.outer(blur_kernel, blur_kernel)

26. ● Perform Wiener deconvolution
restored_image = cv2.filter2D(blurred_image, -1,
blur_kernel)

27. import matplotlib.pyplot as plt
plt.subplot(1, 2, 1)
plt.title("Original")
plt.imshow(blurred_image)
plt.subplot(1, 2, 2)
plt.title("Restored Image")
plt.imshow(restored_image)
plt.show()