Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconArtificial Intelligencebreadcumb forward arrow iconTop 5 Image Processing Projects Ideas & Topics [For Beginners]

Top 5 Image Processing Projects Ideas & Topics [For Beginners]

Last updated:
29th Aug, 2023
Read Time
9 Mins
share image icon
In this article
Chevron in toc
View All
Top 5 Image Processing Projects Ideas & Topics [For Beginners]

In this blog, we will walk through the introduction of image processing and then proceed to talk about a few project ideas that revolve around image processing. 

Image processing is a technique used to perform some operations on the image in order to obtain some meaningful information from them. Here, the input will be an image and after applying a few operations we get an enhanced image or some features associated with these images. 

In image processing, an image is considered as a two-dimensional array of numbers ranging from 0 to 255. Image compression, sharpening, edge-detection are all achieved by using special filters and operators that transform the input image to the output we wish to achieve. For instance, for brightening the image, the operator or filter will behave in a manner that would increase the pixel value of the image.

These operators perform mathematical operations with the 2-D array and produce a new set of output arrays with the desired result. These operations are being extensively used in domains like, Computer vision and Artificial Intelligence, and Machine learning.

Ads of upGrad blog

Moving on, now that we have a basic understanding of what is image processing let us dive into some of the project ideas that can be created by leveraging the aforementioned concept on image processing. 

Top Image Processing Project Ideas

1. Monitoring Social Distancing

With COVID-19 spreading universally, it is prominent to maintain social distancing while travelling in public places. Here image processing can be a game-changer. By taking input from CCTV Cameras and analyzing one frame at a time we will achieve the task at hand. 

Firstly, we use morphological operations and detection techniques to detect pedestrians in a frame. Next, we draw a bounding box surrounding each pedestrian. After which, we calculate the distance of one bounding box enclosing a pedestrian to its adjacent bounding boxes. Next, we decide a threshold for the distance between the bounding boxes and then based on their distance we categorize the pedestrians in the frame as red, yellow, or green.

The red bounding box would mean people in the frame are very close together and therefore at maximum risk. The yellow box would mean that the people are at a considerable distance and the risk is medium. The green boxes would mean people are following the norms and they are safe. Integrating this system with an alerting mechanism (Loudspeakers)could be a great way to alert the pedestrians violating the COVID-19 norms!

FYI: Free nlp course!

2. Mask Detection

Nowadays, wearing masks have been mandatory since the pandemic was discovered. As social distancing, mask detection is equally important to prevent any further surge in COVID cases. To detect mask. we need to first detect the human face. That can be achieved by identifying the facial landmarks such as eyes nose mouth etc. After detecting faces, we need to build an algorithm that can distinguish a face with a mask and a face without a mask.

This calls for the need for a deep learning model. Training a deep learning model on datasets comprising of both mask and non-mask images. Once the model is trained it will be able to successfully identify mask and no-mask people. Using this, we can alert pedestrians to wear masks whenever they step out of their house. 

Also Read: Python Projects on GitHub

3. Lane and Curve Detection 

Autonomous vehicles are the future of driving. With the aim to minimize human intervention and also the potential risk involved, many companies are spending extensively on the Research and Development of autonomous vehicle technologies. By using image segmentation for filtering and edge detection with a deep learning model we detect the presence of lane and their orientation.

A stepwise procedure would look like this

  1. Taking input video as frames.
  2. Converting each frame into its corresponding grayscale image.
  3. Reducing the prevalent noise with the help of filters.
  4. Detecting edges using a canny edge detector.
  5. Finding the coordinates of the road lanes.
  6. Using deep learning to efficiently detect lanes and their orientation.

Best Machine Learning and AI Courses Online

4. Drowsiness Detection for Drivers

The need for drowsiness detection in vehicles is necessary owing to the large number of accidents caused due to lack of consciousness amongst drivers. With a drowsiness detection system, it can alert the driver if it senses a potential loss of consciousness in the eye of the driver. By understanding and analyzing eye patterns, this system can proactively alert the driver and prevent the occurrence of accidents. This task is achieved by first locating and segmenting the eye portion from the rest of the face.

Then binarization and labelling of images are done so as to understand which images represent the occurrence of drowsiness and which don’t. Then by analyzing the blinks and their duration, the algorithm can detect drowsiness if the eyes are closed for a longer time than the time taken to blink the eye. By integrating this system with an alerting device, it could be useful in mitigating the accidents caused due to lack of consciousness. 

In-demand Machine Learning Skills

5. License Plate Recognition

Yes, you heard it right, we can automate the license plate detection. Now the traffic police no longer need to manually pen down the license number of the vehicles violating the traffic rules. Thanks to the advancements in the field of image processing that such a task is possible. The steps that are required for license plate detection include- using appropriate filters to remove noise from the input image and then applying morphological operations on them.

Further, on the region of interest i.e the license plat, we apply a technique known as Optical Character Recognition (OCR)to extract text from the images. OCR is a pretrained network that is capable of detecting text from images. Using it directly will help us save the computation cost of training our algorithm by ourselves. Therefore, by following the above steps systematically, one can develop an algorithm/model to identify the license plate and the number associated with it. 

Checkout: Python Project Ideas & Topics

Image Recognition using Deep Learning

Image recognition, a significant branch of computer vision, involves identifying and classifying objects or patterns within digital images. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have revolutionized image recognition tasks, achieving state-of-the-art performance across various applications.

CNNs are designed to mimic the human visual system, consisting of multiple layers that automatically learn relevant features from images. Here is a step-by-step guide on building an image recognition project using a popular deep learning framework like TensorFlow or PyTorch:

  1. Data Collection: Obtain a labelled dataset of images relevant to your recognition task. For instance, if the project aims to classify cats and dogs, gather a diverse collection of cat and dog images.
  2. Data Preprocessing: Resize all images to a consistent resolution and normalize pixel values to improve training efficiency. Split the dataset into training and testing sets to evaluate model performance accurately.
  3. Model Architecture: Choose a CNN architecture suitable for image recognition. Common choices include VGG, ResNet, or MobileNet. You can also design a custom model tailored to your specific project requirements.
  4. Model Training: Use the training dataset to train the CNN. During training, the model adjusts its internal parameters to minimize the prediction error. This process involves forward propagation, loss computation, and backpropagation to update weights.
  5. Hyperparameter Tuning: Experiment with different hyperparameter values, such as learning rate and batch size, to optimize model performance.
  6. Evaluation: Assess the trained model using the testing dataset. Calculate metrics like accuracy, precision, and recall to gauge how well the model generalizes to new, unseen images.

​7. Deployment: Once satisfied with the model’s performance, deploy it to make real-time predictions on new images.

Image Enhancement and Restoration

Image enhancement and restoration techniques aim to improve the visual quality of images by correcting imperfections caused by noise, low resolution, or other factors. These techniques find applications in various fields, including medical imaging, satellite imagery, and historical photograph restoration.

  1. Contrast Adjustment: This technique enhances the difference between light and dark regions in an image, making it visually more appealing and improving object visibility.
  2. Denoising: Noise is an inevitable part of image acquisition and transmission. Denoising methods, such as median filtering and wavelet denoising, effectively reduce unwanted noise while preserving image details.
  3. Super-Resolution: Super-resolution techniques reconstruct high-resolution images from their low-resolution counterparts, enhancing image clarity and sharpness.

Image enhancement and restoration methods find practical use in numerous real-world projects:

  • In medical imaging, enhancing the visibility of anatomical structures helps physicians make accurate diagnoses and treatment decisions.
  • In satellite imagery, denoising can improve the quality of remote sensing data, leading to better analysis and interpretation of the Earth’s surface.
  • In historical photograph restoration, these techniques aid in preserving old images by reducing degradation effects and restoring missing details, allowing for a glimpse into the past with improved clarity.

As technology advances, image enhancement and restoration continue to play a crucial role in enhancing visual information and enabling a wide range of applications across various industries.


Until now, we have seen 5 examples where image processing can be applied to solve the issue at hand. However, let me tell you that image processing has diversified into almost every industry almost every field is dependent on it directly or indirectly. Because it uses python as its programming language, it is convenient to use and easier to understand.

This post gives you an overview as to what is image processing and few projects associated with it. However, we do encourage you to identify more pressing problems that can be solved by leveraging the concepts of image processing. 

To conclude, developing algorithms pertaining to image processing requires skill and if mastered can help you advance in your professional life at a rapid pace whilst solving real-world problems. 

If you’re interested to join AI and ML Courses, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.

Popular AI and ML Blogs & Free Courses

Ads of upGrad blog

Refer to your Network!

If you know someone, who would benefit from our specially curated programs? Kindly fill in this form to register their interest. We would assist them to upskill with the right program, and get them a highest possible pre-applied fee-waiver up to 70,000/-

You earn referral incentives worth up to ₹80,000 for each friend that signs up for a paid programme! Read more about our referral incentives here.


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
Get Free Consultation

Select Coursecaret down icon
Selectcaret down icon
By clicking 'Submit' you Agree to  
UpGrad's Terms & Conditions

Our Popular Machine Learning Course

Frequently Asked Questions (FAQs)

1What is image processing?

Any field in which images must be altered, edited, and evaluated relies heavily on image processing and computer vision. Remote sensing, medical imaging, autonomous vehicle navigation, and other applications rely on them. Images are typically used as the input and output to image processing operations. In contrast, computer vision usually works with input photos and produces a scene description or categorization as an output. In practice, image processing is performed as low-level computer vision operations, with the input images being filtered before high-level computer vision reasoning is performed.

2What is the importance of image processing?

The significance and requirement of digital image processing originates from two main application areas: the enhancement of input image for human interpretation and the processing of scene data for autonomous machine perception. Remote sensing, picture and data storage for transmission in corporate applications, diagnostic imaging, acoustic imaging, forensic sciences, and industrial automation are only few of the applications of digital image processing. Satellite images are useful for tracking earth resources, topographical mapping, and agricultural crop prediction, as well as weather prediction, flood and fire management.

3What is analogue and digital image processing?

Analogue and digital image processing are the two types of image processing methods employed. Hard copies, such as prints and photographs, can benefit from analogue image processing. When employing these visual tools, image analysts employ a variety of interpretive fundamentals. Digital image processing techniques allow for computer-assisted alteration of digital images. Pre-processing, augmentation, and presentation, as well as information extraction, are the three general processes that all sorts of data must go through when using digital techniques.

Explore Free Courses

Suggested Blogs

Data Preprocessing in Machine Learning: 7 Easy Steps To Follow
Summary: In this article, you will learn about data preprocessing in Machine Learning: 7 easy steps to follow. Acquire the dataset Import all the cr
Read More

by Kechit Goyal

29 Oct 2023

Natural Language Processing (NLP) Projects & Topics For Beginners [2023]
What are Natural Language Processing Projects? NLP project ideas advanced encompass various applications and research areas that leverage computation
Read More

by Pavan Vadapalli

04 Oct 2023

15 Interesting MATLAB Project Ideas & Topics For Beginners [2023]
Learning about MATLAB can be tedious. It’s capable of performing many tasks and solving highly complex problems of different domains. If youR
Read More

by Pavan Vadapalli

03 Oct 2023

Top 16 Artificial Intelligence Project Ideas & Topics for Beginners [2023]
Summary: In this article, you will learn the 16 AI project ideas & Topics. Take a glimpse below. Predict Housing Price Enron Investigation Stock
Read More

by Pavan Vadapalli

27 Sep 2023

Top 15 Deep Learning Interview Questions & Answers
Although still evolving, Deep Learning has emerged as a breakthrough technology in the field of Data Science. From Google’s DeepMind to self-dri
Read More

by Prashant Kathuria

21 Sep 2023

Top 8 Exciting AWS Projects & Ideas For Beginners [2023]
AWS Projects & Topics Looking for AWS project ideas? Then you’ve come to the right place because, in this article, we’ve shared multiple AWS proj
Read More

by Pavan Vadapalli

19 Sep 2023

Top 15 IoT Interview Questions & Answers 2023 – For Beginners & Experienced
These days, the minute you indulge in any technology-oriented discussion, interview questions on cloud computing come up in some form or the other. Th
Read More

by Kechit Goyal

15 Sep 2023

45+ Interesting Machine Learning Project Ideas For Beginners [2023]
Summary: In this Article, you will learn Stock Prices Predictor Sports Predictor Develop A Sentiment Analyzer Enhance Healthcare Prepare ML Algorith
Read More

by Jaideep Khare

14 Sep 2023

Why GPUs for Machine Learning? Ultimate Guide
In the realm of modern technology, the convergence of data and algorithms has paved the way for groundbreaking advancements in artificial intelligence
Read More

by Pavan Vadapalli

14 Sep 2023

Schedule 1:1 free counsellingTalk to Career Expert
footer sticky close icon