Image processing is the method by which the output of an image is enhanced by using different algorithms. The first look of an object catches our attention. Digital image processing has become an interesting field of research for researchers around the world. This is mainly because internet has brought the world closer and has created a huge market for business. So anybody can buy anything from anywhere around the world.

In such a scenario image of your product is the first way in which you can attract your customers. Thus it becomes important to enhance your images by digital image processing methods. Following is a complete overview on DIP project topics. First let us start with the aims of digital image processing.


Image processing is not a new domain. People have been using digital image processing for the following purposes.

  • To enrich one’s visibility.
  • To analyse the details of an image.
  • Whatever is the format (binary, colour, gray scale, multi spectral) of the image, the image processing techniques can be employed to analyse and understand it. 

Our experts have been doing research in DIP project topics for more than 20 years. They insist that understanding the model of image processing is the first step in DIP projects. As they had delivered more than 300+ projects in digital image processing they are very much knowledgeable and proficient in solving any issues associated with it.

So you can get the guidance of our experts for your project. Now let us understand in detail about the working of DIP projects.


The first step in working of digital image processing is to capture signals from any sensors (image) and convert it into digital images. After obtaining digital images the following processes are performed.

  • Improvement in image clarity
  • Removal of artefacts and noises
  • Extraction of different properties of the image (scale, size, objects etc)

For instance, let us consider histogram.

  • It is the most famous and a simple tool for processing images.
  • The image quality is retained in histogram.
  • Values of the pixels are explicit in histogram. 

So the characteristics (entropy illumination contrast signal to noise ratio) of the digital image can be easily modified using it. Our engineers are well experienced in using histogram for processing digital images. Not only histograms they are experts in handling different image processing tools and techniques. So you can approach them for any sort of guidance related to DIP Projects. New let us look into digital image processing elements. 


Acquiring input followed by storing and processing of images and communicating by displaying the output are the various elements or processes of digital image processing. As you might know these processes are huge areas of scope for applications of recent technologies.

 In the research of using advanced technologies for these processes we should not compromise on the quality of images. So now let us have idea on some of the parameters used to assess the quality of an image.


  • Artefacts
  • Noise
  • Resolution of an image (spatial, temporal, contrast)

These are the parameters that can be used to define image quality.

As an example we would highlight the effects of spatial resolution on image quality. Spatial resolution is the direct outcome of how clearly the two smaller objects lying very close to each other can be distinguished separately. By processing of digital images spatial resolution is changed which meaning increasing noise. 

So your digital image processing algorithm and method must be very specific in retaining the image quality. Therefore, choosing the best image processing technique you can seek the advice of our experts. Now let us see about image quality estimation methods. 


The existing methods for estimating the quality of images can be categorised as follows.

  • Objective methods
    • RR
    • FR
      • Metrics of Statistical error 
        • AD
        • PSNR
        • MAE
        • NCC
        • MSE
        • MD
        • SC
      • Metrics based on HVS features
        • SSIM
        • UIQIM
    • NR
      • Pixel
      • Hybrid
  • Subjective methods
    • Single stimulus
    • Double stimulus
      • DSIS
      • DSCQS

Our experts can give you all the basic practical knowledge necessary for handling these methods of image quality estimation. Connect with them at any time for research guidance and support. 

Now Knowledge about the parameters used for assessing image quality is must for doing DIP projects. We are giving you the details of parameters used for assessing image quality below.


The following can be best described as image quality assessing parameters.

  • Kurtosis – represents image data on normal distribution
  • Entropy – quantity of data to be coded for algorithm attributed to compression
  • Lateral chromatic aberration – focusing image colours at particular distances.
  • Blemishes – sensor dusts leading to marks in the image
  • Contrast sensitivity – distinguishable image-object contrast relation
  • Noise – occurs due to image density variation
  • Veiling glare – flaring due to reflection within lens elements
  • Distortion of lens – lines get curved due to lens distortion
  • Reproducing tone – brightness of the image before and after processing must be retained
  • Image gradient – colour and intensity of the image with respect to direction
  • Variance – distinguishes one pixel from another
  • ISO sensitivity and accuracy of exposure – easily determined by cameras adjusted manually
  • Light fall off – also called as vignetting. It darkens the image edges
  • Spatial resolution – it is the function of point and spread factors
  • Color moire – occurs due to repeating spatial frequencies.
  • Sharpness – changes due to shaking of camera, focus and sensor accuracy, Changes in atmosphere.
  • Artefacts – loss in contrast, noise, data compression, loss during transmission 
  • Accuracy in colour – colour is the vague factor for image quality determination 

These are the parameters that can be used to check the quality of images. All the projects that we delivered specifically give immense importance to these parameters. As a result our projects were very successful. You can get in touch with our developers and know more about the technical details of the projects that we delivered. Now let us have some idea on research areas in digital image processing.


The following are the research areas in digital image processing.

  • Technologies for biometrics
  • Robotic vision
  • Underwater processing of images
  • Visual systems of humans
  • Medical imaging

We provide you project and research assistance on all these topics. We often ask our customers to enrich their knowledge on latest technologies and on-going researches their topic. To stand by that point we are providing the details of latest DIP project topics below.


The latest research topics in digital image processing are given below,

  • Similarity detection between skull and digital face
  • Detection of fake colorized image
  • Detection of JPEG grid (automatic)
  • Splicing (localization and detection)
  • Analysis of video evidence
  • Identifying and analysing forensic writer
  • Image steganography (based on secret key)
  • Digital image forensic (copy and move)
  • Detection of auto-image forgery

We provide assistance for research project development, thesis writing, assignment and paper publication on all these topics. Connect with us to get more details. Our engineers are keeping them up to date by learning to work on the latest topics in image processing. As a result they have acquired greater knowledge in these fields. Now let us look into the tools for image processing.



  • Simple CV – python wrapper (openCV)
  • Scikit – image (image processing algorithm collections)
  • PIL or Python Image Library – handling and processing of images


  • OpenCV Filters (basic to advanced image processing)
  • Standard Filters (inbuilt filters for processing)
  • MATLAB (allows for faster prototyping, easy to use, error handling and troubleshooting)
  • ImageJ Filters (skeletonization of image processing)
  • GPU Filters (openGL pixelshaders for GPU power harnessing)
  • DIY Filters (perform iterations and operations on images)


  • FilterForge (filters are created using dataflow programming language based on node) – it is used as adobe photoshop plugin and has about 10,000 DIP filters

Expert with us have worked using these processing tools. They have also registered huge success in their attempts. So you can connect with us at any time for any kind of research support in digital image processing projects.