Image processing is the field of signal processing where both the input and output signals are images. Images can be thought of as two-dimensional signals via a matrix representation, and image processing can be understood as applying standard one-dimensional signal processing techniques to two-dimensional signals.
Image processing is a very important subject, and finds applications in such fields as photography, satellite imaging, medical imaging, and image compression, just to name a few. In the past, image processing was largely done using analog devices. However, as computers have become more powerful, processing shifted toward the digital domain.
Like one-dimensional digital signal processing, digital image processing overcomes traditional analog “problems” such as noise, distortion during processing, inflexibility of system to change, and difficulty of implementation. The image processing technique we will be implementing will be
image blurring.
Image Processing Techniques using MATLAB
As the board we have does not support a direct connection for the input image, we will use MATLAB to output the image as a matrix and store it in the data memory of the DSP. To do this, we will use the parallel port connection to get our input data into the board. The DSP will then do the processing and write the output data in the program memory. We extract the output data and go back to MATLAB to analyze the results.
Our project methodology includes the following:
1. Use MATLAB to simulate the processing technique.
2. Implement the technique with assembly language on a TMS320C542-based DSKplus board to perform the same operation.
3. Carefully locating the memory blocks where we will store our original and output image.
4. Comparing our results from MATLAB and DSP outputs.
Source: University of California
Authors: Cheng, Herman | Huang, Zhicong | Kumimoto, Mark
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