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It is the initial step in the process of image processing and it has a simple style which is like having an image in digital form. Image acquisition involves preprocessing, which is the scaling of the image. The process begins by using a sensor to capture the image followed by its digitization. If the output of the sensor is not in a digital format, an analogue-to-digital converter is used. The image needs a proper acquisition to carry the objective forward. Currently, the 3D acquisition method is the inclination of students for research and dissertation purposes.
The image enhancement is the easiest part of the image processing journey. With image enhancement, the details that get obscured are found while highlighting the required features of the image. Changing the contrast or brightness of the image is an example of image enhancement. To put it simply, it is the modification of images to get the desired result. There is an advanced development in processing the algorithms for image enhancement with the inclusions of images’ brightness, size, and contrast. The students prefer this topic because its impacts a human’s perception of the image.
Image restoration is the process of bringing back the quality of the image. Image enhancement is highly subjective, while image restoration is purely objective. The models and techniques of image restoration are based on mathematical and probabilistic models of the image degradation process. All the noises and blurs from the image are removed by image restoration to present an original and clean image. For students doing M.Tech in image processing, this can be a good choice for dissertations. The information lost in the process of image restoration is recovered through the reversal process.
Colour image processing is the inclination of many students because of the popularity of digital images on social media. The colour image processing needs a digital domain for the colour modelling process. A 3D coordinate system is a base to specify colour values, for instance, the RGB model, HSI model, and the CMY model. Humans can perceive tons of colours. Hence, colour image processing is done for an image. The two areas of colour image processing with a good scope of research are pseudo-colour processing and full-colour processing.
To represent the image in various resolutions, wavelets come into play. The process of image subdivision is to break the image into segments for pyramidal representation and data compression. Wavelets break the data into multiple components which have different frequencies. It is a mathematical function. A resolution matching scale helps in studying the components individually. In image processing, multi-image processing is a pyramidal process that increases the utilization of multi-resolution techniques. Students with an inclination towards technical aspects can choose this topic for the dissertation.
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