A Novel Architecture for Computing Approximate Radon Transform
This work addresses a compute-intensive bottleneck in image processing applications, but it appears incremental as it builds on existing approximate algorithms.
The paper tackles the problem of high computational complexity in computing the Radon transform by proposing a novel architecture that uses sequential memory access and pipelining to reduce time complexity.
Radon transform is a type of transform which is used in image processing to transfer the image into intercept-slope coordinate. Its diagonal properties made it appropriate for some applications which need processes in different degrees. Radon transform computation needs a lot of arithmetic operations which makes it a compute-intensive algorithm. In literature an approximate algorithm for computing Radon transform is introduces which reduces the complexity of computations. But this algorithm is complex and need arbitrary accesses to memory. In this paper we proposed an algorithm which accesses to memory sequentially. In the following an architecture is introduced which uses pipeline to reduce the time complexity of algorithm.