digital image processing thesis

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Digital image processing

• Masters Thesis
• Ha, Vinh Thuc
• Wong, Robert
• Bavarian, Behzad
• Electrical and Computer Engineering
• California State University, Northridge
• Dissertations, Academic -- CSUN -- Engineering.
• 2017-04-11T18:16:09Z
• http://hdl.handle.net/10211.3/189576
• by Vinh Thuc Ha
• California State University, Northridge. Department of Engineering.
• Includes bibliographical references (page 59)

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Top 10 Digital Image Processing Project Topics

We guide research scholars in choosing novel digital image processing project topics. What is meant by digital image processing? Digital Image Processing is a method of handling images to get different insights into the digital image. It has a set of technologies to analyze the image in multiple aspects for better human / machine image interpretation . To be clearer, it is used to improve the actual quality of the image or to abstract the essential features from the entire picture is achieved through digital image processing projects.

This page is about the new upcoming Digital Image Processing Project Topics for scholars who wish to create a masterpiece in their research career!!!

Generally, the digital image is represented in the form of pixels which are arranged in array format. The dimension of the rectangular array gives the size of the image (MxN), where M denotes the column and N denotes the row. Further, x and y coordinates are used to signify the single-pixel position of an image. At the same time, the x value increases from left to right, and the y value increases from top to bottom in the coordinate representation of the image. When you get into the DIP research field, you need to know the following key terminologies.

Important Digital Image Processing Terminologies  

• Stereo Vision and Super Resolution
• Multi-Spectral Remote Sensing and Imaging
• Digital Photography and Imaging
• Acoustic Imaging and Holographic Imaging
• Computer Vision and Graphics
• Image Manipulation and Retrieval
• Quality Enrichment in Volumetric Imaging
• Color Imaging and Bio-Medical Imaging
• Pattern Recognition and Analysis
• Imaging Software Tools, Technologies and Languages
• Image Acquisition and Compression Techniques
• Mathematical Morphological Image Segmentation

Image Processing Algorithms

In general, image processing techniques/methods are used to perform certain actions over the input images, and according to that, the desired information is extracted in it. For that, input is an image, and the result is an improved/expected image associated with their task. It is essential to find that the algorithms for image processing play a crucial role in current real-time applications. Various algorithms are used for various purposes as follows, 

• Digital Image Detection
• Image Reconstruction
• Image Restoration
• Image Enhancement
• Image Quality Estimation
• Spectral Image Estimation
• Image Data Compression

For the above image processing tasks, algorithms are customized for the number of training and testing samples and also can be used for real-time/online processing. Till now, filtering techniques are used for image processing and enhancement, and their main functions are as follows, 

• Brightness Correction
• Contrast Enhancement
• Resolution and Noise Level of Image
• Contouring and Image Sharpening
• Blurring, Edge Detection and Embossing

Some of the commonly used techniques for image processing can be classified into the following, 

• Medium Level Image Processing Techniques – Binarization and Compression
• Higher Level Image Processing Techniques – Image Segmentation
• Low-Level Image Processing Techniques – Noise Elimination and Color Contrast Enhancement
• Recognition and Detection Image Processing Algorithms – Semantic Analysis

Next, let’s see about some of the traditional image processing algorithms for your information. Our research team will guide in handpicking apt solutions for research problems . If there is a need, we are also ready to design own hybrid algorithms and techniques for sorting out complicated model . 

Types of Digital Image Processing Algorithms

• Hough Transform Algorithm
• Canny Edge Detector Algorithm
• Scale-Invariant Feature Transform (SIFT) Algorithm
• Generalized Hough Transform Algorithm
• Speeded Up Robust Features (SURF) Algorithm
• Marr–Hildreth Algorithm
• Connected-component labeling algorithm: Identify and classify the disconnected areas
• Histogram equalization algorithm: Enhance the contrast of image by utilizing the histogram
• Adaptive histogram equalization algorithm: Perform slight alteration in contrast for the  equalization of the histogram
• Error Diffusion Algorithm
• Ordered Dithering Algorithm
• Floyd–Steinberg Dithering Algorithm
• Riemersma Dithering Algorithm
• Richardson–Lucy deconvolution algorithm : It is also known as a deblurring algorithm, which removes the misrepresentation of the image to recover the original image
• Seam carving algorithm : Differentiate the edge based on the image background information and also known as content-aware image resizing algorithm
• Region Growing Algorithm
• GrowCut Algorithm
• Watershed Transformation Algorithm
• Random Walker Algorithm
• Elser difference-map algorithm: It is a search based algorithm primarily used for X-Ray diffraction microscopy to solve the general constraint satisfaction problems
• Blind deconvolution algorithm : It is similar to Richardson–Lucy deconvolution to reconstruct the sharp point of blur image. In other words, it’s the process of deblurring the image.

Nowadays, various industries are also utilizing digital image processing by developing customizing procedures to satisfy their requirements. It may be achieved either from scratch or hybrid algorithmic functions . As a result, it is clear that image processing is revolutionary developed in many information technology sectors and applications.  

Digital Image Processing Techniques

• In order to smooth the image, substitutes neighbor median / common value in the place of the actual pixel value. Whereas it is performed in the case of weak edge sharpness and blur image effect.
• Eliminate the distortion in an image by scaling, wrapping, translation, and rotation process
• Differentiate the in-depth image content to figure out the original hidden data or to convert the color image into a gray-scale image
• Breaking up of image into multiple forms based on certain constraints. For instance: foreground, background
• Enhance the image display through pixel-based threshold operation 
• Reduce the noise in an image by the average of diverse quality multiple images 
• Sharpening the image by improving the pixel value in the edge
• Extract the specific feature for removal of noise in an image
• Perform arithmetic operations (add, sub, divide and multiply) to identify the variation in between the images 

Beyond this, this field will give you numerous Digital Image Processing Project Topics for current and upcoming scholars . Below, we have mentioned some research ideas that help you to classify analysis, represent and display the images or particular characteristics of an image.

Latest 11 Interesting Digital Image Processing Project Topics

• Acoustic and Color Image Processing
• Digital Video and Signal Processing
• Multi-spectral and Laser Polarimetric Imaging
• Image Processing and Sensing Techniques
• Super-resolution Imaging and Applications
• Passive and Active Remote Sensing
• Time-Frequency Signal Processing and Analysis
• 3-D Surface Reconstruction using Remote Sensed Image
• Digital Image based Steganalysis and Steganography
• Radar Image Processing for Remote Sensing Applications
• Adaptive Clustering Algorithms for Image processing

Moreover, if you want to know more about Digital Image Processing Project Topics for your research, then communicate with our team. We will give detailed information on current trends, future developments, and real-time challenges in the research grounds of Digital Image Processing.

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What is Digital Image Processing?

Digital image processing is the process of using computer algorithms to perform image processing on digital images. Latest topics in digital image processing for research and thesis are based on these algorithms. 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”. The history of digital image processing dates back to early 1920s when the first application of digital image processing came into news. Many students are going for this field for their  m tech thesis  as well as for Ph.D. thesis. There are various thesis topics in digital image processing for M.Tech, M.Phil and Ph.D. students. The list of thesis topics in image processing is listed here. Before going into  topics in image processing , you should have some basic knowledge of image processing.

Latest research topics in image processing for research scholars:

• The hybrid classification scheme for plant disease detection in image processing
• The edge detection scheme in image processing using ant and bee colony optimization
• To improve PNLM filtering scheme to denoise MRI images
• The classification method for the brain tumor detection
• The CNN approach for the lung cancer detection in image processing
• The neural network method for the diabetic retinopathy detection
• The copy-move forgery detection approach using textual feature extraction method
• Design face spoof detection method based on eigen feature extraction and classification
• The classification and segmentation method for the number plate detection
• Find the link at the end to download the latest thesis and research topics in Digital Image Processing

Formation of Digital Images

Firstly, the image is captured by a camera using sunlight as the source of energy. For the acquisition of the image, a sensor array is used. These sensors sense the amount of light reflected by the object when light falls on that object. A continuous voltage signal is generated when the data is being sensed. The data collected is converted into a digital format to create digital images. For this process, sampling and quantization methods are applied. This will create a 2-dimensional array of numbers which will be a digital image.

Why is Image Processing Required?

• Image Processing serves the following main purpose:
• Visualization of the hidden objects in the image.
• Enhancement of the image through sharpening and restoration.
• Seek valuable information from the images.
• Measuring different patterns of objects in the image.
• Distinguishing different objects in the image.

Applications of Digital Image Processing

• There are various applications of digital image processing which can also be a good topic for the thesis in image processing. Following are the main applications of image processing:
• Image Processing is used to enhance the image quality through techniques like image sharpening and restoration. The images can be altered to achieve the desired results.
• Digital Image Processing finds its application in the medical field for gamma-ray imaging, PET Scan, X-ray imaging, UV imaging.
• It is used for transmission and encoding.
• It is used in color processing in which processing of colored images is done using different color spaces.
• Image Processing finds its application in machine learning for pattern recognition.

List of topics in image processing for thesis and research

• There are various in digital image processing for thesis and research. Here is the list of latest thesis and research topics in digital image processing:
• Image Acquisition
• Image Enhancement
• Image Restoration
• Color Image Processing
• Wavelets and Multi Resolution Processing
• Compression
• Morphological Processing
• Segmentation
• Representation and Description
• Object recognition
• Knowledge Base

1. Image Acquisition:

Image Acquisition is the first and important step of the 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 an image by the sensor (such as a monochrome or color TV camera) and digitized. In case, the 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 master’s thesis and research.

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 the 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 a blur, noise from images to produce a clean and original image. It can be a good choice for the M.Tech thesis on image processing. The image information lost during blurring is restored through a reversal process. This process is different from the 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 the 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 a 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. It is an interesting topic in image processing.

Mangosteen Quality Grading for Export Markets Using Digital Image Processing Techniques

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Digital Image Processing And Machine Learning Research: Digital Color Halftoning, Printed Image Artifact Detection And Quality Assessment, And Image Denoising.

To begin with, we describe a project in which three screens for Cyan, Magenta, and Yellow colorants were designed jointly using the Direct Binary Search algorithm (DBS). The screen set generated by the algorithm can be used to halftone color images easily and quickly. The halftoning results demonstrate that by utilizing the screen sets, it is possible to obtain high-quality color halftone images while significantly reducing computational complexity.

Our next research focuses on defect detection and quality assessment of printed images. We measure and analyze macro-uniformity, banding, and color plane misregistration. For these three defects, we designed different pipelines for them and developed a series of digital image processing and computer vision algorithms for the purpose of quantifying and evaluating these printed image defects. Additionally, we conduct a human psychophysical experiment to collect perceptual assessments and use machine learning approaches to predict image quality scores based on human vision.

We study modern deep convolutional neural networks for image denoising and propose a network designed for AWGN image denoising. Our network removes the bias at each layer to achieve the benefits of scaling invariant network; additionally, it implements a mix loss function to boost performance. We train and evaluate our denoising results using PSNR, SSIM, and LPIPS, and demonstrate that our results achieve impressive performance on both objective and subjective IQA assessments.

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• Electrical and Computer Engineering

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              Digital image processing thesis topics are actively chosen these days, considering the scope of the topic in the near future. Here is a detailed understanding of doing projects in digital image processing . Digital image processing is the process by which digital images are modified according to the user’s wish.  Initially, images are an array of two-dimensional points arranged into columns and rows. First, let us start with its working. It can be made into the following.

• Black and white image
• 8-bit image
• 16-bit color format

HOW DOES DIGITAL IMAGE PROCESSING WORK?

It is one of the most fundamental questions that have to be answered before dwelling deep into the topic. Digital image processing projects are the favourite area of research for our expert team. They are currently guiding several projects on advanced image processing in the digital world. They suggest the following steps as the basic functionality of digital image processing.

• Acquiring inputs in the form of both video and image
• Analysis of the input
• Extraction of useful information from it through manipulation 
• Processing of output
• Reporting of the final output

In this way, the digital image processing method works. Your project can have an objective to improve upon these steps by implementing current developments like AI and Internet of Things projects into it. Do you feel it would be easy for you if someone already experienced in the field of digital

Image processing methods research helps you in your project? If so, then you have found the right place to get assistance from. We provide the best online research guidance for projects related to digital image processing . We have the most favoured online research experts who can help you do the best projects on the topic. Please continue reading to know more about our digital image processing projects.

WHAT ARE THE OPERATIONS IN DIGITAL IMAGE PROCESSING?

As you might know, there are various processes involved in the techniques of digital image processing. We are currently developing projects on all these steps. Our projects mostly spiral around these topics with the aim of improving their efficiency. The following are the  different steps involved in the functioning of digital image processing .

• Image retrieval (extracting useful images from the input)
• Detecting objects (object recognition)
• Extraction of content (essential content is extracted)
• Image preprocessing (denoising, restoring, enhancing contrast, etc.)
• Detection of the object (object recognition)

These steps are very significant for the processing of digital images. Algorithms are developed so as to achieve more efficiency in each of these steps.  These algorithms can be evaluated based on the performance and the quality of output obtained . Our technical team is building various methods to enhance image quality. 

We provide you support for digital image processing thesis topics too. Our developers and writers are well qualified and are highly experienced in producing standard theses and summaries. So you can rely on them for any support regarding your dip thesis . Now let us look into some of the performance metrics used for evaluating the algorithms used in digital image processing .

HOW IS IMAGE QUALITY MEASURED?

The quality image is a direct outcome of the algorithm used for processing digital images. The evaluation of such algorithms is based on the following factors.

• ROC and AUC curve
• Recall 
• Sensitivity
• Precision 
• Specificity
• Accuracy 

All our projects have shown great results with respect to these metrics. You can get in touch with us to know more about the projects that we delivered. We will provide you the details of the performance of our projects when they were implemented in real-time dip projects using python . 

Along with these metrics, some pre-and post-processing metrics should look upon to design your project on digital image processing . Let us see about those metrics in the following.

PERFORMANCE ANALYSIS IN DIGITAL IMAGE PROCESSING

PREPROCESSING METRICS

The following are the preprocessing metrics used in evaluating digital image processing methods.

• Root mean squared error or RMSE
• Structural Similarity Index or SSIM
• Patch-based contrast quality index or PCQI
• Blink or reference less image spatial quality evaluator or BRISQUE
• Mean Squared Error or MSE
• Peak Signal to Noise Ratio or PSNR
• In contrast to noise ratio or CNR
• Colour image Quality Measure or CIQM

Your project should focus on showing good results with respect to these metrics. Our engineers can guide you in such a way to achieve greater results in performance metrics. Get in touch with us and have a talk with our experts on choosing your digital image processing thesis topics . We will now provide you details of post-processing metrics.

POSTPROCESSING METRICS

The following post-processing metrics have to be remembered in the case of digital image processing techniques

• Kappa quadratic weight
• Kappa coefficient 
• Mean absolute error
• Accuracy(total)
• Kappa linear weight
• Root mean square error
• Rate of error
• Confusion matrix

When your research project excels in these measurements, your project will be appreciated. We are ready to stand by your side to make your project a huge success. Now let us see about some important research ideas in digital image processing.

RESEARCH IDEAS IN DIGITAL IMAGE PROCESSING  THESIS TOPICS

The following are the most important areas of research in digital image processing based on the current trends. 

• Detecting number plate (segmentation and classification)
• Detecting lung cancer (CNN approach)
• Autonomous navigation
• Advanced and recent methods for processing images
• Compression of video and image(for reducing size) 
• Scene understanding
• Detecting copy-move forgery (by extracting textual feature)
• Detection of diabetic retinopathy by neural network method
• Multiple object detection
• Face spoof detection (method of extracting eigen feature)

We have reviewed and monitored projects with these metrics. World-class experts with us are highly experienced in writing your thesis so as to show better results in these metrics. As algorithms for this performance efficiency basis , let us see more about the different types of algorithms that are popular in digital image processing.

IMPORTANT ALGORITHMS FOR DIGITAL IMAGE PROCESSING

The following are the standard algorithms for digital image processing

• Conditional GANs
• Deep convolutional GANs 

Currently, very few experts in handling these algorithms around the world  are well experienced in dealing with these algorithms. They are updating them every now and then to make themselves undeniable choices for research support in digital image processing. Now let us see in more detail about digital image processing projects using MATLAB in image analysis.

WHAT IS IMAGE ANALYSIS IN MATLAB? 

MATLAB plays a key role in Analysing images on the following grounds.

• Detection of edges
• Counting (objects)
• Shape finding
• Noise removal
• Calculation of statistics (analysis of texture and quality of the image)

You might have been more familiar with using MATLAB.  Our engineers have been phenomenal in handling MATLAB techniques for many ideal case applications.  So you can know more about the practical difficulties that they faced and the ways in which they overcame these issues and made their projects more ideal than others. 

IMAGE PROCESSING TECHNIQUES FOR IMAGE ANALYSIS

Extraction of useful information from an image is called image analysis. The following are the categories of image analysis.

• Region analysis (extraction of statistical information)
• Segmentation of image (for distinguishing objects and regions)
• Removing noise (with deep learning and morphological filtering)
• Enhancement of image (displaying and analyzing images)

MATLAB functions are quite popular for usage in analyzing medical images . Let us see about the functions of MATLAB used for image analysis in the following section.

MATLAB FUNCTIONS FOR IMAGE ANALYSIS

The following MATLAB functions are used for image analysis.

• bwselect3 (selection of objects)
• imgradientxyz (finding 3D image direction and magnitude of gradient)
• imhist (image histogram data)
• edge3 (3D intensity volume – finding edges)
• imgradientxyz (finding direction gradients of 3D images)
• regionprops3 (measurement of volume of regions in 3D volumetric images)

Now let us see more about MATLAB functions for the segmentation of images.

MATLAB FUNCTIONS FOR IMAGE SEGMENTATION

There are some critical MATLAB functions used for image segmentation. They are listed below.

• Bfscore (outlines image segmentation score)
• Gradientweight (calculation of weights)
• Imsegfmm (segmentation of binary image)
• Jaccard (finding Jaccard similarity coefficient)
• Active contour (segmentation of images on fore and background)
• Dice (for Sorensen-dice similarity coefficient)
• Graydiffweight (image pixel weight calculation)
• imsegkmeans3 (volume segmentation based on k-means clustering)
• superpixels3 (oversegmentation of 3D superpixel)

Our experts can give you complete support and guidance in any digital image processing thesis topic . You can reach out to us regarding any type of research support, and we here provide you details of all basics about digital image processing. Advanced ideas are also readily available with us. We will stay with you in your entire research journey.

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Digital Platform for Construction of Environmental and Economic Water Resource Maps

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• First Online: 05 March 2024
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• A. I. Semyachkov 14 ,
• Yu. O. Slavikovskaya 14 &
• V. A. Pochechun 14  

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 400))

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• International Conference on Construction, Architecture and Technosphere Safety

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Today, for balanced environmental management, it is necessary to consider and assess the availability and condition of natural resources and the environment quality on time. To accomplish the goals specified, this paper suggests using the databases characterizing the water resource management in Ural, Siberian, and Far Eastern Federal Districts to prepare the environmental and economic maps that reflect the water resource availability, quality, and use efficiency as well as the adverse affecting factors. The maps were created on the basis of public statistics as well as the data provided by the companies using natural resources. The maps were made with the statistics collection and analysis methods to develop the indicators that reflected the intensity, efficiency, and environmental friendliness of water resource use as well as the software products like Microsoft Excel and Surfer. The research results can be used by water management and environmental protection specialists as well as federal and regional authorities to develop water management and protection programs.

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A. I. Semyachkov, Yu. O. Slavikovskaya & V. A. Pochechun

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Semyachkov, A.I., Slavikovskaya, Y.O., Pochechun, V.A. (2024). Digital Platform for Construction of Environmental and Economic Water Resource Maps. In: Radionov, A.A., Ulrikh, D.V., Timofeeva, S.S., Alekhin, V.N., Gasiyarov, V.R. (eds) Proceedings of the 7th International Conference on Construction, Architecture and Technosphere Safety. ICCATS 2023. Lecture Notes in Civil Engineering, vol 400. Springer, Cham. https://doi.org/10.1007/978-3-031-47810-9_54

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DOI : https://doi.org/10.1007/978-3-031-47810-9_54

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