Image Processing Homework Help


Image processing seems like a simple phenomenon at first glance, one might think of it as just capturing a picture and then modifying it. However, in reality, if we try to understand the background process, it is not that simple.

There is an interfacing stage which connects the real-time images with a computer.Image processing initiates with capturing an image, this is done by a transducer, which converts the color intensities into analog signals. In image processing, the camera or camcorder is our transducer which transforms the real objects into raw signals.To transmit an image, size may deter the transmission rate, therefore the image is compressed to reduce its size to a suitable value.

After compression, the image is transmitted through the data bus to the processing unit, then the real processing begins. The processing unit may not be able to directly process the raw analog signals, hence an interfacing unit is required. The primary purpose of the unit is to amplify and filter the signals because the signals have low magnitude and may have instrumentation noise as well. Just like any other form of data, computer processes the image in digital form that is why the analog signals are digitized. The advent of image transmitting applications has increased the importance of image processing exponentially.

These advancements have perfectly overlapped with data sciences, therefore, image processing has introduced another dimension in the developing domain of artificial intelligence.Analyzing processing an image as a whole object demands a huge amount of processing time and computations, image is therefore divided into a form of an array, where a single unit of the array is termed as Pixel.The computer then further digitizes the pixels by scaling their intensities between one and zero, where zero corresponds to black and one to white.

Image processing algorithms are then applied to digital pictures to extract useful information by evaluating each pixel at a time. Image processing draws its contribution from diverse domains such as Signal Processing, Neural Networks, and Data Acquisition. Image processing has numerous applications, from medical purposes to criminal detection, image processing may serve its purpose.

As discussed above, image processing requires inter-disciplinary algorithms and techniques and MATLAB offers the functionality of all the required disciplines, hence rendering MATLAB as an optimal tool for image processing. MATLAB easily performs the functions of image processing such as; enhancing, cropping, and filtering. Simulink provides specific and dedicated functions for image segmentation, filtering, plotting, enhancement, and transformation.

The basic unit for processing for MATLAB is a matrix, therefore, image is also transformed and stored in matrices form by MATLAB. The software has numerous functions for matrix, hence, making it convenient to process an image. An image can be processed in two basic categories; grayscale and colored. Simulink’s functions are applicable on both the categories, however, processing a grayscale image is simple task while a color image possesses little complexity to be processed. MATLAB storesa grayscale image in a 2-D array of 256*256 (65536 values), however the 2-D array for color image contains 256*256*3 (196608 elements) because colored image is formed by three basic colors (Red, Blue, and Green).

There is a parameter in Simulink through which image size is reduced but, consequently, image quality is decreased as there is a trade-off. Simulink provides the functionality of replicating an image without even reading it, this is done by initializing an array whose elements correspond to the intensities of the image, consequently, complexity and processing time of image acquisition is mitigated.

There is a whole toolbox in MATLAB dedicated for image processing which contains multiple basic functions. After applying algorithms on a picture, it is required to save an image for future use, it is done by write function of Simulink, it does the reverse of reading an image.

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