Digital Image Processing or DIP is one of the most trending areas of research as well as for thesis. There are a number of topics in digital image processing in which a student can go for deep research and can put forward a new theory. Before going into thesis topics for image processing let us discuss the basics of digital image processing – what it is and why it is used.
What is Digital Image Processing?
Digital Image Processing is a subfield of signals that deals with the alteration of digital images to refine its features and characteristics. The operations on images are performed using efficient algorithms specially designed for this purpose. The input is an image and the output is also an image or its extracted features. There are different techniques for image processing that are applied to digital images to extract information from the images. Following are the three main steps of image processing:
- Import the image using image acquisition tools
- Analyze and manipulate the image
- Alter the image
Applications of Digital Image Processing
Digital Image Processing has a number of useful applications. Some of the major applications are as follows:
- Color Processing
- Video Processing
- Pattern Recognition
- Remote Sensing
- Medical Imaging
- Image Restoration
- Image Sharpening
- Robot Vision
- Microscopic Imaging
- Satellite Imaging
Trending Areas for Thesis and Research in Digital Image Processing
As earlier said there are a lot of areas in digital image processing in which a student can for higher education and research. There are also interesting topics in digital image processing for the thesis. Reseach scholars can write a thesis on image processing using MATLAB simulation tool. Following is the list of trending areas for thesis and research in Digital Image Processing:
- Image Segmentation
- Image Compression
- Image Classification
- Image Denoising
- Forensic Image Processing
- Image Acquisition
- Image Restoration
- Object detection and recognition
Image Segmentation is a technique of partitioning an image into different segments with each segment having pixels of nearly same attributes. The main purpose of image segmentation is to make an image more meaningful to analyze. Labels are assigned to each pixel in the image so as to identify pixels with similar characteristics. The prominent applications of image segmentation include machine vision, content-based image retrieval, object detection, face recognition, video surveillance, etc. It is a very good area for the thesis on image processing.
Thresholding method is the commonly used and the simplest method for image segmentation. K-means algorithm is used to partition an image into different clusters. Other main methods of image segmentation include color-based segmentation, watershed segmentation, multispectral segmentation, texture methods.
Image Compression is a technique of minimizing the size of an image without degrading its quality. It is another good area for research and thesis in image processing. Data compression algorithms are used to perform image compression on images. The commonly used image compression techniques are:
- Transform coding
- Run-length encoding
- Chroma subsampling
Image compression can be of two types – lossy or lossless. Lossy methods are used for natural images while lossless methods are used for medical imaging. JPEG and GIF are the common compressed graphics image formats. PNG is the new format that is going to replace these obsolete formats.
Image Classification is a technique in which land cover classes or themes are assigned to the pixels. Information is extracted from these classes to create thematic maps of the land cover present in the image. Image Classification is an essential part of the digital image analysis and interesting area for research. Generally, there are two types of classification methods:
- Supervised Classification – In this method, representative samples are selected for each land cover class which are which are used as training samples to classify an image.
- Unsupervised Classification – In this classification, the pixels are grouped together into clusters to create cluster analysts.
An image classifier is used to distinguish one class from the another. Following techniques are used to assign pixels to classes:
- Artificial Neural Network(ANN)
- Maximum Likelihood(ML)
- Support Vector Machines(SVM)
Image denoising is a part of the image analysis process in which noise is removed from the original image to produce a better quality image. A noise is an undesirable signal that interferes with the original signal and degrades the quality of the image. A noise in images can occur due to transmission, compression, imperfect instruments etc. Noise can cause blurring in images and is a challenging task for researchers to tackle. There are different techniques for image denoising which are as follows:
- Linear Filters
- Spatial Filtering
- LMS Adaptive Filter
- Median Filter
- Weighted Median Filter
- Wavelet Transforms
- Mallat’s Algorithm
- Wavelet Thresholding
Skeletonization is a process to reduce foreground details in a binary image to represent a general form of an object. There are certain algorithms used for the process of skeletonization. Morphological thinning is used to eliminate pixels from the boundary. Following are the three main processes of skeletonization:
- Converting the original image into feature and non-feature elements. The feature elements are along the boundary of the object.
- Generating the distance map that provides the distance between the element and the nearest feature element.
- The local extreme points are detected as the skeletal points.
It is one of the trending topics in digital image processing for the thesis. For skeletal decomposition, a morphological approach is followed to decompose a complex shape into simple components. It is the popular method to represent a morphological shape. Other main algorithms for skeletonization include Generalized Skeleton Transform, Octagon-Fitting Algorithm, Morphological Filtering, Shape Reconstruction, etc.
Forensic Image Processing
Forensic Image Processing or FIP is a technique to enhance surveillance imagery using computer restoration techniques. The main purpose of this technique is to extract more information from noisy images and surveillance imagery. It improves the quality of digital images to a certain level using various computer-based methods. In FIP, the pixel values are changed to enhance the image quality. It finds its application in crime detection to analyze crime scenes through fingerprints and footmarks. The surveillance imagery is used in banks, ATMs, hospitals, universities, shopping malls, traffic signals. The enhancement of this surveillance is done through Forensic Image Processing(FIP) technology.
Image Acquisition is a process of retrieving an image from source usually a hardware source. The image thus acquired is an unprocessed image. It is the first stage of a vision system. A single sensor like photodiode can be used for Image Acquisition. The motion should be in both x and y directions to obtain a 2D image from a single sensor. Image Acquisition can also be done through line sensor and array sensor. Initial setup and long-term maintenance of the hardware is the major factor in the image acquisition process. Real-time image acquisition is also one of the forms of image acquisition. This area has a tremendous scope for research.
Image Restoration is the process of creating a clean, original image by performing operations on the degraded image. The degradation can be blur, noise which diminishes the quality of the image. In image restoration, the process that blurred the image is reversed to obtain the original image. This process is entirely different from the process of image enhancement in the sense that image enhancement improves the features of the image. Following are the main methods of image restoration process:
- Inverse Filter
- Weiner Filtering
- Wavelet Restoration
- Blind Deconvolution
Object Detection and recognition
Object detection and recognition system in images are defined as web-based applications whose aim is to detect the multiple objects from various types of images. Recognition is done after the image performing the detection. Object detection and recognition are similar techniques to identify an image but having different execution methods. Object detection method helps to find the instance of objects in images. Object detection is defined as the subset of object recognition, where the object is not only identified but also located in an image. This allows multiple objects to be identified and located within the same image.
Watermarking is a technique used to hide information inside digital media and is mainly used as a method for copyright protection. The digital media can be text, image, video, or audio. The process of digital image watermarking is categorized into the following three stages:
- Embedding Stage
- Distortion Stage
- Retrieval Stage
There are various techniques used for Digital Image Watermarking which are:
- Spatial Domain Watermarking
- Transform Domain Watermarking
- Discrete Cosine Transform
- Discrete Wavelet Transform
- Discrete Fourier Transform
MATLAB tool is used to implement these techniques of Digital Image Watermarking. The main applications of Digital Image Watermarking include:
- Owner Identification
- Copyright Protection
- Data Authentication
- Broadcast Monitoring
- Medical Applications
Steganography is the process of embedding information in some other file for security purposes. Only authorized sender and receiver will be aware of the hidden data in this case. The main attributes of steganography are:
Steganography techniques can be further classified into different categories which are:
- Pure Steganography – No encryption is done and the secret message is hidden in the cover image.
- Secret Key Steganography – Higher security and the sender and the receiver have the secret keys.
- Public Key Steganography – A pair of public and private keys is used to hide the secret message. It provides robustness and key management.
Steganography is a trending area for thesis and research in image processing.
List of Thesis Topics in Digital Image Processing
Here is the list of latest thesis topics in digital image processing using MATLAB:
- To propose a hybrid technique for image classification to analyze properties of satellite images.
- The hybrid technique for edge detection using bio-inspired techniques.
- Enhancement in SVD hybrid technique to increase image PNSR value using a 2D Kalman filter
- Plant disease detection using neural networks approach.
- Enhancement in image compression techniques using signal processing algorithms.
- A novel algorithm for detection of arbitrarily oriented text in an image
- To enhance the security of watermarking using face and iris recognition
- To drive an automated system for FAR and Distorted Number plate recognition
- To propose an improvement in probability based Object Tracking algorithm to detect dissimilar types of objects.
- To evaluate and enhance SIFT algorithm to check the authenticity of Indian currency
- To propose an improvement in the water-shed algorithm for Cancer detection
- The hybrid algorithm based on SIFT and SVM classifier for Forgery detection
- To Improve Z-H algorithm for dynamic conditions to increase Thinning Rate in skeletonization
- The hybrid technique for image classification to detect Gender and Age.
- To drive an automated system to detect dust patterns from source camera.
- To propose and evaluate filter based on internal and external features of an image for image denoising
These were some of the current topics in digital image processing for M.Tech and Ph.D. thesis as well as for research. You can contact us for any kind of thesis-related help on any these topics. Writemythesis provides full thesis guidance on Image Processing.