Perhaps the great French military leader Napolean Bonaparte wasn't too far off when he said, “A picture is worth a thousand words.” Ignoring the poetic value, if just for a moment, the facts have since been established to prove this statement's literal meaning. Humans, the truly visual beings we are, respond to and process visual data better than any other data type. The human brain is said to process images 60,000 times faster than text. Further, 90 percent of information transmitted to the brain is visual. These stats alone are enough to serve the importance images have to humans.

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Therefore, the domain of image processing, which deals with enhancing images and extracting useful information from them, has been growing exponentially since its inception. This process has developed and augmented popular platforms and libraries like MATLAB, scikit-image, and OpenCV. The technology forerunners and the world-renowned conglomerates such as Google, Apple, Microsoft, and Amazon dabble in Image processing. Therefore, the hands-on experience working on image processing projects can be an invaluable skill.

20+ Image Processing Projects Ideas

With the vast expectations the domain bears on its shoulders, getting started with Image Processing can unsurprisingly be a little intimidating. As if to make matters worse for a beginner, the myriad of high-level functions implemented can make it extremely hard to navigate. Since one of the best ways to get an intuitive understanding of the field can be to deconstruct and implement these commonly used functions yourself, the list of image processing projects ideas presented in this section seeks to do just that! 

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Image Processing Projects for Beginners

This section has easy image processing projects ideas for novices in Image processing. You will find this section most helpful if you are a student looking for image processing projects for the final year.

1) Grayscaling Images

Grayscaling is among the most commonly used preprocessing techniques as it allows for dimensionality reduction and reduces computational complexity. This process is almost indispensable even for more complex algorithms like Optical Character Recognition, around which companies like Microsoft have built and deployed entire products (i.e., Microsoft OCR).

The output image shown above has been grayscaled using the rgb2gray function from scikit-image. (Image used from Image Processing Kaggle)

There are plenty of readily available functions in OpenCV, MATLAB, and other popular image processing tools to implement a grayscaling algorithm. For this image processing project, you could import the color image of your choice using the Pillow library and then transform the array using NumPy. For this project, you are advised to use the Luminosity Method, which uses the formula 0.21*R+0.72*G+0.07*B. The results look similar to the Grayscale image in the figure with minor variations in contrast because of the difference in the formula used.  Alternatively, you could attempt to implement other Grayscaling algorithms like the Lightness and the Average Method.

2) Image Smoothing

Image smoothing ameliorates the effect of high-frequency spatial noise from an image. It is also an important step used even in advanced critical applications like medical image processing, making operations like derivative computation numerically stable.

For this beginner-level image processing project, you can implement Gaussian smoothing. To do so, you will need to create a 2-dimensional Gaussian kernel (possibly from one-dimensional kernels using the outer product) by employing the NumPy library and then convoluting it over the padded image of your choice. The above output has been obtained from the scikit-image with the Multi-dimensional Gaussian filter used for smoothing. Observe how the ‘sharpness' of the edges is lost after the smoothing operation in this image processing project. The smoothing process can also be performed on the RGB image. However, a grayscale image has been used here for simplicity.

3) Edge Detection

Edge detection helps segment images to allow for data extraction. An edge in an image is essentially a discontinuity (or a sharp change) in the pixel intensity values of an image. You must have witnessed edge detection at play in software like Kingsoft WPS or your own smartphone scanners and, therefore, should be familiar with its significance.

For this project, you can implement the Sobel operator for edge detection. For this, you can use OpenCV to read the image, NumPy to create the masks, perform the convolution operations, and combine the horizontal and vertical mask outputs to extract all the edges.

The above image demonstrates the results obtained by applying the Sobel filter to the smoothed image. 

NOTE: On comparing this to the results obtained by applying the Sobel filter directly to the Grayscaled image (without smoothing) as shown below, you should be able to understand one of the reasons why smoothing is essential before edge detection.

4) Skew Correction

Skew correction is beneficial in applications like OCR. The pain of skew correction is entirely avoided by having artificial intelligence-enabled features built into applications like Kingsoft WPS.

You can try using OpenCV to read and grayscale the image to implement your skew correction program. To eliminate the skew, you will need to compute the bounding box containing the text and adjust its angle. An example of the results of the skew correction operation has been shown. You can try to replicate the results by using this Kaggle dataset ImageProcessing. 

5) Image Compression using MATLAB

Quoting Stephen Hawking, “A picture is worth a thousand words...and uses up a thousand times the memory.” Despite the advantages images have over text data, there is no denying the complexities that the extra bytes they eat up can bring. Optimization, therefore, becomes the only way out.

If words alone haven't made the case convincing enough, perhaps the mention of the startup, Deep Render, which is based on applying machine learning to image compression, raising £1.6 million in seed funding, should serve to emphasize the importance of this domain succinctly.

For this MATLAB Image Processing project, you can implement the discrete cosine transform approach to achieve image compression. It is based on the property that most of the critical information of an image can be described by just a few coefficients of the DCT. You can use the Image Processing Toolbox software for DCT computation. The input image is divided into 8-by-8 or 16-by-16 blocks, and the DCT coefficients computed, which have values close to zero, can be discarded without seriously degrading image quality. You can use the standard ‘cameraman.tif' image as input for this purpose. 

Image Credit: Mathworks.in 

Intermediate Image Processing Projects Ideas

In the previous section, we introduced simple image processing projects for beginners. We will now move ahead with projects on image processing that are slightly more difficult but equally interesting to attempt.

6) Image Effect Filters 

You must have come across several off-the-shelf software capable of cartooning and adding an artistic effect to your images. These features are enabled on popular social media platforms like Instagram and Snapchat. Producing images with effects of your liking is possible by using Neural Style Transfer.

To implement a model to achieve Neural Style Transfer, you need to choose a style image that will form the ‘effect' and a content image. (You can use this dataset: Tamil Neural Style Transfer Dataset  for this image processing project.) The feature portion of a pre-trained VGG-19 can be used for this purpose, and the weighted addition of the style and content loss function is to be optimized during backpropagation.

7) Face Detection

Seldom would you find a smartphone or a camera without face detection still in use. This feature has become so mainstream that most major smartphone manufacturers, like Apple and Samsung, wouldn't explicitly mention its presence in product specifications.

For this project, you could choose one of the following two ways to implement face detection.

1. Image Processing Projects using OpenCV Python

For this approach, you could use the pre-trained classifier files for the Haar classifier. While this is not particularly hard to implement, there is much to learn from precisely understanding how the classifier works.

2. Deep Learning Approach

While dlib's CNN-based face detector is slower than most other methods, its superior performance compensates for its longer execution time. To implement this, you can simply use the pre-trained model from //dlib.net/files/mmod_human_face_detector.dat.bz2.

Irrespective of the approach you choose to go about with for the face detection task, you could use this Image Processing Random Faces Dataset on Kaggle.

8) Image to Text Conversion using MATLAB

Image-to-text conversion or Optical Character Recognition has been the basis of many popular applications such as Microsoft Office Lens and a feature of others such as Google documents. The prevalence of OCR systems is only rising as the world becomes increasingly digitized. Therefore, this digital image processing project will involve familiarizing yourself with accomplishing image-to-text conversion using MATLAB!

While converting image to text by Optical Character Recognition can be pretty easy with other programming languages like Python (for instance, using pyTesseract), the MATLAB implementation of this project can seem slightly unfamiliar. But unfamiliarity is all there is to this otherwise simple application. You can simply use the Computer Vision Toolbox to perform Optical Character Recognition. Additionally, you can use the pre-trained language data files in the OCR Language Data support files from the OCR Engine page, Tesseract Open Source OCR Engine. Further, as an extension of this project, you could try training your own OCR model using the OCR Trainer application for a specific set of characters, such as handwritten characters, mathematical characters, and so on.

Some publicly available datasets you could use for training on handwritten characters include Digits 0-9: MNIST, A-Z in CSV format, and Math symbols.

9) Watermarking

Watermarking is a helpful way to protect proprietary images and data; it essentially signs your work the way painters do to their artwork. For digital watermarking to be effective, it should be imperceivable to the human eye and robust to withstand image manipulations like warping, rotation, etc.

For this project, you can combine Discrete Cosine Transform and Discrete Wavelet Transform for watermarking. You can implement an effective machine learning algorithm for watermarking by changing the wavelet coefficients of select DWT sub-bands followed by the application of DCT transform. Operations like DCT can be accomplished in Python Data Science Tutorial using the scipy library.

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10) Image Classification using MATLAB

Image Classification finds wide digital applications (for instance, it is used by Airbnb for room type classification) and industrial settings such as assembly lines to find faults, etc. 

One way to achieve image classification with MATLAB using the Computer Vision Toolbox function is by employing a visual bag of words. This involves extracting feature descriptors from the training data to train the classifier. The training essentially consists of converting the extracted features into an approximated feature histogram based on the likeness or closeness of the descriptors using clustering to arrive at the image's feature vector. This classifier is then used for prediction. To start this task, you could use this (adorable!) cat, dog, and panda classifier dataset.

Advanced Python Image Processing Projects with Source Code

It is time to level up your game in image processing. After working on the above mentioned projects, we suggest you try out the following digital image processing projects using Python.

11) Background Subtraction

Background subtraction is an important technique used in applications like video surveillance. As video analysis involves frame-wise processing, this technique naturally finds its place in the image processing domain. It essentially separates the elements in the foreground from the background by generating a mask. 

To model the background, you will need to initialize a model to represent the background characteristics and update it to represent the changes such as lighting during different times of the day or the change in seasons. The Gaussian Mixture Model is one of the many algorithms you can use for this image processing project. (Alternatively, you can use the OpenCV library, which has some High-level APIs which will significantly simplify the task. However, it is advised to understand their working before using them.) The dataset made available on: http://pione.dinf.usherbrooke.ca/  can be used with due acknowledgments.

12) Instance Segmentation

While object detection involves finding the location of an object and creating bounding boxes, instance segmentation goes a step beyond by identifying the exact pixels representing an instance of every class of object that the machine learning model has been trained. Instance segmentation finds its use in numerous state-of-the-art use cases like self-driving cars.

It is advised to use Mask RCNN for this image segmentation problem. You can use the pre-trained mask_rcnn_coco.h5 model and then provide an annotated dataset. The following miniature traffic dataset is annotated in COCO format and should aid transfer learning.

Source Code:Image Segmentation using Mask RCNN Data Science Project

13) Pose Recognition using MATLAB

Pose estimation finds use in augmented reality, robotics, and even gaming. The computer vision or deep learning-based company, Wrnch, is based on a product designed to estimate human pose and motion and reconstruct human shape digitally as two or three-dimensional characters.

Using the Open Pose algorithm, you can implement pose estimation with the Deep Learning Toolbox in MATLAB. The pre-trained model can be used from //ssd.mathworks.com/supportfiles/vision/data/human-pose-estimation.zip. (For this project, you can use the MPII Human Pose Dataset (human-pose.mpi-inf.mpg.de/).

14) Medical Image Segmentation

Image processing in the medical field is a topic whose benefits and scopes need no introduction. Healthcare product giants like Siemens Healthineers and GE Healthcare have already headed into this domain by introducing AI-Rad Companion and AIRx (or Artificial Intelligence Prescription), respectively.

To accomplish the medical image segmentation task, you can consider implementing the famous U-Net architecture; a convolutional neural network developed to segment biomedical images using the Tensorflow API. While there have been many advancements and developments since U-Net, the popularity of this architecture and the possible availability of pre-trained models will perhaps help you get started. 

Using the two chest x-rays datasets from Montgomery County and Shenzhen Hospital, you can attempt lung image segmentation: hncbc.nlm.nih.gov/LHC-downloads/dataset.html. Alternatively, you can use the masks for the Shenzhen Hospital dataset.  

Source Code:Medical Image Segmentation

15) Image fusion

IImage fusion combines or fuses multiple input images to obtain new or more precise knowledge. In medical image fusion, combining multiple images from single or multiple modalities reduces the redundancy and augments the usefulness and capabilities of medical images in diagnosis. Many companies (like ImFusion) provide expert services, and others develop in-house solutions for their image processing use cases, such as image fusion.

For your image fusion project, you can consider Multifocus Image Fusion.You can use the method described by Slavica Savić's paper titled “Multifocus Image Fusion Based on Empirical Mode Decomposition” for this. The following publicly available multi-focus image datasets can be used to build and evaluate the solution:  github.com/sametaymaz/Multi-focus-Image-Fusion-Dataset.

Image-Processing Projects using Python with Source Code on GitHub

This section is particularly for those readers who want solved projects on image processing using Python. We have mentioned the GitHub repository for each project so that you can understand the implementation of the projects deeply.

16) Sudoku Solver

During summer holidays in childhood, most of us would have at least once tried playing the exciting game- Sudoku. It is common to get stuck when playing the game for the first time. But, the most challenging part is to go back and realize where one went wrong?

Well, if you are into image processing, you can build a project to solve the sudoku.

The project idea is to build an intelligent sudoku solver that can pull out the sudoku game from an input image and solve it. And the first step in creating such an application is to apply a gaussian blur filter to the image. After that, use adaptive gaussian thresholding, invert the colors, dilate the image, and use a convolutional neural network to recognize the puzzle. The last step is then to use mathematical algorithms to solve the puzzle.

FUN FACT: Sudoku is an abbreviation for “suuji wa dokushin ni kagiru” (japanese), which means “the numbers must remain single.” 

Source Code on GitHub: GitHub Repository:neeru1207/AI_Sudoku

17) Bar-Code Detection

This is one of the fun digital image processing projects you should try. It will introduce you to exciting and intriguing image processing techniques while guiding you on building a system that can detect bar codes from an image.

The idea behind this project is simple. One must make a computer locate that area in an image with maximum contours. You will first need to convert the image into grayscale to get started. After that, use image processing methods like gradient calculation, blur image, binary thresholding, morphology, etc., and finally find the area with the highest number of contours. Then, label the area as a bar code. Additionally, you can use OpenCV to decode the barcode.

Source Code on GitHub: pyxploiter/Barcode-Detection-and-Decoding

18) Automatically Correcting Images’ Exposure

Instagram is one of the top 6 social networks with more than a billion users, and we hope you are not surprised. It's the era of social media platforms, and photographs/digital images are one of the best ways to convey what's going on in your life. And, where there are images, there are filters to beautify them.

In this project, you will build a system that can automatically correct the exposure of an input image. This project will help you understand image processing techniques like Histogram Equalisation, Bi-Histogram based Histogram Equalisation, Contrast Limited Adaptive Histogram Equalisation, Gamma Transformation, Adaptive Gamma Transformation, Weighted Adaptive Gamma Transformation, Improved Adaptive Gamma Transformation, and Adaptive non-linear Stretching.

Source Code on GitHub: GitHub - 07Agarg/Automatic-Exposure-Correction

19) Quilting Images and Synthesising Texture

While creating images on platforms like Canva, one often comes across images with great texture but do not have a high resolution. Well, through image processing techniques, you can easily create a solution for such images.

This project will create an application that will take a textured image as input and extend that texture to form a higher resolution image. Additionally, you will use the texture and overlap it over another image, referred to as image quilting. This project will use various image processing methods to pick the right texture and create the desired images. You will understand how different mathematical functions like root-mean-square are utilized over pixels for images.

Source Code on GitHub: GitHub - ani8897/Image-Quilting-and-Texture-Synthesis

20) Signature Verifying System

Can you recall those childhood days when you'd request your siblings to sign your leave application on your parents' behalf by forging their signatures? Forging signatures sounds like a funny thing when you are a kid but not as an adult. Someone can withdraw money from your bank account without you knowing it by forging your signature. So, how do banks make sure it's you and nobody else? You guessed it right; they use image processing.

In this project, you evaluate the score difference between two images of signatures; one would be the original, and the other would be the test image. You will learn how to apply deep learning models like CNN, SigNet, etc., on processed images to build the signature verification application.

Source Code on GitHub: GitHub - DefUs3r/Automatic-Signature-Verification

Concluding with a quote from George Bernard Shaw, “The only way to learn something is to do something.” While (digital) image processing and machine learning were long established in his time, it doesn't make his advice any less applicable. Projects, short and fun as they are, are a great way to improve your skills in any domain. So, if you've made it up to here, make sure you don't leave without taking up an image processing project or two, and before you know it, you'll have the skills and the project portfolio to show for it!

FAQs on Image Processing Projects

What is Image Processing with Example?

Image processing is a method for applying operations on an image to enhance or extract relevant information. It's a form of signal processing in which the input is an image, and the output is either that image or its features. Example: Grayscaling is a popular image processing technique that reduces computational complexity while minimizing dimensionality.

What can be done with image processing?

Image processing has various applications such as Pattern Recognition, Video processing, Machine/Robot Vision, Image sharpening and restoration, Color processing, Microscopic Imaging, etc.

Which is the best software for image processing?

Here is a list of the best software for image processing:

• Adobe Photoshop

• GIMP

• Pixelmator

• Inkscape

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