A Novel Architecture for Computing Approximate Radon Transform

TL;DR

This paper introduces a new pipeline architecture for computing an approximate Radon transform that reduces computational complexity and improves memory access efficiency in image processing tasks.

Contribution

The paper presents a novel sequential memory access algorithm and a pipelined architecture to enhance Radon transform computation efficiency.

Findings

Reduced computation time through pipelining.

Sequential memory access improves efficiency.

Potential for real-time image processing applications.

Abstract

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.