DIP Projects using Python performs different digital approaches on digital images through an automated system for deep investigation of images. Moreover, in Information Technology based industries, image processing has special recognition in developing computer vision applications. Basically, the digital image is made up of pixels that signify the color contribution of the image. By the by, each pixel is pointed out as a single unit in the image, which addresses the RGB value.  

On this page, you can get to know the new important updates on the Digital Image Processing research field!!!

Research DIp Projects Using Python Code

Why is Python used for Image Processing?

Many researchers or developers prefer Python as the best choice for handling and operating image-oriented processes in recent times since it attains the respectable evolution in the field of DIP for its availability and programmability.  

In general, image processing is an enriched research area that has futuristic computer vision/graphics developments. Consequently, scholars are profoundly working on mathematical operations to design new approaches to enhance image quality.

  • Face Recognition
  • Handwritten Recognition
  • Automated Image Segmentation

Algorithms for DIP Projects 

Most practicably, many DIP Projects using Python utilizes machine learning algorithms and techniques for technical analysis of the image. Truly, it is widely spread in several research domains such as artificial intelligence, healthcare, real-world applications, agriculture, etc. Also, it is applied in the form of reinforcement learning, unsupervised learning, and supervised learning for various operations like 

  • Noise Reduction
  • Feature Selection
  • Quality Enhancement
  • Object Detection
  • Action Recognition
  • Image Denoising

For your information, here we have selected the following four approaches with their commonly used algorithms,

For Object Detection 

  • You Only Look Once (YOLO) 
  • Mask Region-based CNN (Mask-RCNN)
  • Fast Region-based CNN (Fast R-CNN)
  • Faster Region-based CNN (Faster R-CNN)

For Object Recognition

  • Inception Networks
  • Residual Network (ResNeXts)
  • Sqeeze and Excitation Networks (SENets)

For Action Recognition, 

  • Temporal Segment Networks 
  • Dynamic Image Networks
  • Semantic Image Networks

For Image Denoising 

  • Fast and Flexible Network based on Denoising CNN (FFDNet)
  • Residual Network (ResNeXts)
  • Convolutional Neural Network (CNNs) 
  • Denoising Convolutional Neural Network (DnCNN)

Suppose you are implementing the digital image processing projects using a python programming language. In that case, you need to know the following libraries, which simplify the work carried out in the complex algorithms.

Top 7 Python toolbox Image Processing projects

Top 7 Image Processing Libraries in Python

  • SimplelTK
  • Pillow
  • OpenCV
  • Matplotlib
  • SciPy
  • Mahotas
  • Scikit-image

We have long-lasting experience in dealing with DIP Projects using Python. So, our developers have strong sound knowledge of different libraries. Each one has different functionalities for executing specific needs. So, you need to be confident with your project requirements to select a suitable library for better image processing.

What is the best image processing library to use in Python?

OpenCV

OpenCV is an open-source and broad-range collection of libraries for performing video and image-oriented processes efficiently. Due to its cross-platform capability and flexibility, it is widely spread in machine learning and computer vision operations such as face detection, image dissection, image analysis, object finding, and many more. For instance: a face recognition system is highly used in many country’s railway stations to find the lawbreakers.

In addition, it also turns out to be an essential entity of AI and data science workflow for collecting abstract information from images/videos. Similar to the OpenCV library, there are more pre-defined repositories and archives. 

Python Libraries for DIP 

PIL/pillow

PIL is an open-source, powerful library that is expanded as Python Image Library, where Pillow is the friendly PIL fork. Majorly, it can perform various kinds of image formats such as JPEG / JPG, PNG, PPM, BMP, GIF, and TIFF. Also, it is used to perform operations like image cropping, resizing, grayscaling, rotating, etc. In order to do these operations, an image module in the library is used, which has the following networking python functions.

  • open() – Load image.
  • save() – Store image in png format for Pillow
  • format attribute – Represent file format
  • show() – View / Display image
  • rotate() – Rotate image by the degree of rotation
  • size attributes – Represent image size
  • resize() – Alter image size by height and width value 
  • mode attribute – Represent pixel format
  • transform() – Flip image by any of the following properties,
    • Image.ROTATE_270
    • Image.FLIP_LEFT_RIGHT
    • Image.ROTATE_90
    • Image.FLIP_TOP_BOTTOM
    • Image.ROTATE_180
  • crop() – Crop image by size of area and position  

NumPy

NumPy is a library that supports structural data to work with mathematical functions like linear algebra. Also, the data is represented in the form of multi-dimensional or n-dimensional array format as NdArray type. For instance: the RGB color image separates the color into red channel, green channel, and blue channel through numpy 3D-array. As well, it carries out other image processing methods such as analyzing, extracting, and flipping images. Below are few important operations that executed using NumPy,

  • imread(test_img) – Input image (where test_img is variable name)
  • test_img[::-1] – Reverse image use 
  • np.fliplr(test_img) – Horizontal flip of image 
  • np.flipud(test_img) – Vertical flip of image 
  • test_img[:,:,2] – Get blue channel from RGB channel
  • test_img[:,:,1] – Get green channel from RGB channel
  • test_img[:,:,0] – Get red channel from RGB channel 
  • np.where(test_img > 150, 255, 0) – Add filter to the image 
  • (if value is 150, replace with 255 and if value is not 255 the replace with 0)

All these libraries are commonly used in many DIP Projects using Python. In recent days, scholars are looking forward to working with medical images since it consists of numerous neuro-project topics starting from neuro-imaging to mammogram / MRI imaging. For example, cancer detection.

Beyond these libraries, other free, user-friendly image processing libraries are used for dip based python projects. If you are new to some libraries, then we will help you to work with them. For your information, we have listed few more interesting research ideas for processing digital images.

Latest Research Topics in Image Processing

  • Efficient prediction of environmental changes through ML-based segmentation technique on satellite images
  • AI-based feature extraction for efficient Keratoconus disease prediction 
  • Efficient prediction of weather conditions by using machine learning approaches on satellite images 

Enhance the feature selection in the image classification process 

  • through Hybrid optimization approaches
  • Medical image-based ML techniques for the precise disease forecast 

On the whole, we are ready to serve you Dip projects using python in research, code development, manuscript writing (proposal, literature survey, paper, and thesis/dissertation). So, hold your hands with us to create an extraordinary research journey.