What is Digital Image Processing?
Digital image processing is the process of using computer algorithms to perform image processing on digital images. Being a subcategory of digital signal processing, digital image processing is better and carries many advantages over analog image processing. It permits to apply multiple algorithms to the input data and does not cause the problems such as the build-up of noise and signal distortion while processing. As images are defined over two or more dimensions that make digital image processing “a model of multidimensional systems”. Many students are going for this field for their master’s thesis as well as for PhD thesis. There are various thesis topics in digital image processing for M.Tech, M.Phil and PhD students. The list of thesis topics in image processing are listed here.
List of topics in image processing for thesis and research
1. Image Acquisition:
Image Acquisition is the first and important step of digital image of processing. Its style is very simple just like being given an image which is already in digital form and it involves preprocessing such as scaling etc. It starts with the capturing of image by sensor (such as a monochrome or color TV camera) and digitized. In case, output of the camera or sensor is not in digital form then an analog-to-digital converter (ADC) digitizes it. If the image is not properly acquired, then you will not be able to achieve tasks that you want to. Customized hardware is used for advanced image acquisition techniques and methods. 3D image acquisition is one such advanced method image acquisition method. Students can go for this method for their masters thesis.
2. Image Enhancement:
Image enhancement is one of the easiest and the most important areas of digital image processing. The core idea behind image enhancement is to find out information that is obscured, or to highlight specific features according to the requirements of an image. Such as, changing brightness & contrast etc. Basically, it involves manipulation of an image to get desired image than original for specific applications. Many algorithms have been designed for the purpose of image enhancement in image processing to change an image’s contrast, brightness and various other such things. Image Enhancement aims to change the human perception of the images. Image Enhancement techniques are of two types: Spatial domain and Frequency domain.
3. Image Restoration:
Image restoration involves improving the appearance of an image. In comparison to image enhancement which is subjective, image restoration is completely objective which makes the sense that restoration techniques are based on probabilistic or mathematical models of image degradation. Image restoration removes any form of blur, noise from images to produce a clean and original image. It can be a good choice for m.tech thesis on image processing. The image information lost during blurring is restored through a reversal process. This process is different from image enhancement method. Deconvolution technique is used and is performed in the frequency domain. The main defects that degrade an image are restored here.
4. Color Image Processing:
Color image processing has been proved to be of great interest because of the significant increase in use of digital images on the Internet. It includes color modeling and processing in a digital domain etc. There are various color models which are used to specify a color using 3D coordinate system. These models are RGB Model, CMY Model, HSI Model, YIQ Model. The color image processing is done as humans can perceive thousands of colors. There are two areas of color image processing full color processing and pseudo color processing. In full color processing, the image is processed in full colors while in pseudo color processing the grayscale images are converted to colored images.
5. Wavelets and Multi Resolution Processing:
Wavelets act as a base for representing images in varying degrees of resolution. Images subdivision means dividing images into smaller regions for data compression and for pyramidal representation. Wavelet is a mathematical function using which the data is cut into different components each having different frequency. Each component is the then studied separately through resolution matching scale. Multi resolution processing is a pyramid method used in image processing. Use of multi resolution techniques are increasing. Information from images can be extracted using multi resolution framework.
Compression involves the techniques that are used for reducing storage necessary to save an image or bandwidth to transmit it. If we talk about its internet usage, it is mostly used to compress data. Algorithms acquire useful information from images through statistics to provide superior quality images. Image compression is a trending thesis topic in image processing.
7. Morphological Processing:
Morphological processing involves extracting tools of image components which are further used in the representation and description of shape. There are certain non-linear operations in this processing that relates to the features of the image. These operations can also be applied to grayscale images. The image is probed on a small scale known as structuring element.
Segmentation involves dividing an image into its constituent parts or objects. Generally, autonomous image segmentation is one of the toughest tasks in digital image processing. It is a rugged segmentation procedure that takes a long way toward successful solution of imaging problems that require objects to be identified individually. In simple terms, image segmentation means partitioning an image into multiple segments for simplification and changing the representation of image. In this, a label is assigned to every pixel such two or more labels may share the same label.
9. Representation and Description:
The behavior of representation and description depends on the output of a segmentation stage and it includes raw pixel data, constituting either all the points in the reign or only boundary of the reign. Choosing a representation is a part of solution to transform raw data into a suitable form that allows subsequent computer processing. As description deals with extracting attributes that yield quantitative information of interest or basic to separate one class from another.
10. Object recognition:
Recognition involves assigning of a label, such as, “vehicle” to an object completely based on its descriptors. It is a method of recognising a specific object in an image or video. There are certain techniques and models for object recognition like deep learning models, bag-of-words model etc. This can be done using Matlab. This requires machine learning and deep learning methods. Go for this topic for your m.tech thesis on image processing.
11. Knowledge Base:
Knowledge is all about detailing regions of an image to locate the information of interest that ultimately delimits the research to be conducted in seeking that information. Knowledge Base becomes complex such as an interconnected list of all major possible defects in materials assessment problems or an image database carrying high-resolution satellite images of a region in relation with change-detection applications.
These are some of the list of thesis topics in image processing. There are also various thesis topics in digital image processing using Matlab as Matlab tool is the most common tool used for image processing. Contact Techsparks for thesis help in Image Processing.