Radon Transform and Dynamic Stochastic Resonance based Technique for Line Detection from Noisy Images

EasyChair Preprint 959, version 1

Abstract

The Radon transform is an important transform to detect line feature from the noisy image. Radon transform can transform two-dimensional images (with noisy or disturbed lines) into a domain of possible parameters of line, where each line in the image will give a peak position at the corresponding parameters of the line. It has led to many line detection applications within image processing, computer vision, earthquake engineering etc. When the lines are subjected to very high background noises, Radon transform alone is not so effective.

Here, in this paper, we propose dynamic stochastic resonance (DSR) based Radon transform for weak line extraction. The DSR is an iterative process that tunes the coefficient of Radon transform so that we may get the enhanced lines of the image. We compare our proposed method with the results of the Gaussian low pass filter. The proposed technique adopts local adaptive processing, and it significantly enhances the line feature of an image. Experimental results are also given to show the effectiveness of the proposed method.

Keyphrases: Discrete Cosine Transform, Dynamic Stochastic Resonance, Noise induced resonance, Radon transform, line detection

@booklet{EasyChair:959,
  author    = {Rajib Kumar Jha and Badal Soni and Sumit Kumar and Vivek Singh Verma},
  title     = {Radon Transform and Dynamic Stochastic Resonance based Technique for Line Detection from Noisy Images},
  howpublished = {EasyChair Preprint 959},
  year      = {EasyChair, 2019}}