Whether it's capturing a car's license plate on the highway or detecting someone's facial features to tag friends, computer vision and image processing have found their way into many facets of our lives. Image and video processing algorithms ultimately tailor towards one of two goals: to analyze data and produce output in as close to real-time as possible, or to take in and operate on large swaths of information offline. Image convolution is a mathematical method with which we can filter an image to highlight or make clearer desired information. The most popular uses of image convolution accentuate edges, corners, and facial features for analysis. The goal of this project was to investigate various image convolution algorithms and compare them in terms of hardware usage, power utilization, and ability to handle substantial amounts of data in a reasonable amount of time. The algorithms were designed, simulated, and synthesized for the Zynq-7000 FPGA, selected both for its flexibility and low power consumption.

Image convolution is a common algorithm that can be found in most graphics editors. It is used to filter images by multiplying and adding pixel values with coefficients in a filter kernel. Previous research work have implemented this algorithm on different platforms, such as FPGAs, CUDA, C etc. The performance of these implementations have then been compared against each other. When the algorithm has been implemented on an FPGA it has almost always been with a single convolution. The goal of this thesis was to investigate and in the end present one possible way to implement the algorithm with 16 parallel convolutions on a Xilinx Spartan 6 LX9 FPGA and then compare the performance with results from previous work. The final system performs better than multi-threaded implementations on both a GPU and CPU.

The Diploma thesis deals with reconstruction of Missing data of an Image. It is done by the use of appropriate Mathematical theory and numerical algorithm to reconstruct missing information. The result of this implementation is the reconstruction of missing image information. The thesis also compares different numerical methods, and see which one of them perform best in terms of efficiency and accuracy of the given problem, hence it is used for the reconstruction of missing data.

Memristor is a novel passive electronic device and a promising candidate for new generation non-volatile memory and analog computing. Analog computing based on memristors has been explored in this study. Due to the lack of commercial electrical testing instruments for those emerging devices and crossbar arrays, we have designed and built testing circuits to implement analog and parallel computing operations. With the setup developed in this study, we have successfully demonstrated image processing functions utilizing large memristor crossbar arrays. We further designed and experimentally demonstrated the first memristor based field programmable analog array (FPAA), which was successfully configured for audio equalizer and frequency classifier demonstration as exemplary applications of such memristive FPAA (memFPAA).