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Image Denoising and Refinement Based on an Iteratively Reweighted Least Squares Filter

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This paper presents a method to reduce noise and refine detail features of a scene based on an iteratively reweighted least squares method. The performance of the proposed filter, called the iteratively reweighted least squares filter (IRLSF), was compared with the state-of-the-art filters by checking their ability to recover simulated edge models under various degrees of noise contamination. The results of the simulation comparison show that IRLSF is superior to the other filters in terms of its ability to recover the original edge models. To apply IRLSF to real images of a scene captured by a camera, a procedure composed of corner detection, least squares matching, bilinear resampling, and iteratively reweighted least squares is proposed. The experimental results show that IRLSF produces mean images that are effectively denoised, and that its accuracy is less than one half of grey-level-quantization-unit of test images captured by a commercial camera.

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This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1B02011625).

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Correspondence to Suyoung Seo.

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Seo, S. Image Denoising and Refinement Based on an Iteratively Reweighted Least Squares Filter. KSCE J Civ Eng 24, 943–953 (2020). https://doi.org/10.1007/s12205-020-2103-x

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  • Denoising
  • Least squares matching
  • Iteratively reweighted least squares