Image De-noising on Strip Steel Surface Defect Using Improved Compressive Sensing Algorithm
Abstract: De-noising for the
strip steel surface defect image is conductive to the accurate detection of the
strip steel surface defects. In order to filter the Gaussian noise and salt and
pepper noise of strip steel surface defect images, an improved compressive
sensing algorithm was applied to defect image de-noising in this paper. First,
the improved Regularized Orthogonal Matching Pursuit algorithm was described.
Then, three typical surface defects (scratch, scar, surface upwarping) images
were selected as the experimental samples. Last, detailed experimental tests
were carried out to the strip steel surface defect image de-noising. Throughcomparison
and analysis of the test results, the Peak Signal to Noise Ratio value of the
proposed algorithmis higher compared with other traditional de-noising
algorithm, and the running time of the proposed algorithm is only26.6% of that
of traditional Orthogonal Matching Pursuit algorithms. Therefore, it has better
de-noising effect and can meet the requirements of real-time image processing.
Author: Dongyan Cui
Journal Code: jptkomputergg170022
