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Quantitative Restoration of Noisy Colour Texture Segmentation Benchmark Images using State-of-the-Art Algorithm
, B. Sheela Rani, M. Sutaone
Published in Institute of Electrical and Electronics Engineers Inc.
Pages: 37 - 42
Texture segmentation is a well-known research domain and a large number of researchers are working on it across the globe due to its wide varieties of applications in various domains such as medical imaging, shape extraction, product inspection, remote sensing, and segmentation of natural images. Many times, images are corrupted by various types of noises such as Gaussian, speckle, and salt-pepper noise. Most often Gaussian noise is a source of corruption in many applications. Prague texture dataset is extensively used by researchers due to wide varieties of multi-class textures in it. The present study offers the restoration of Prague texture database benchmark images. These images are corrupted with Gaussian noise with different variance values. The state-of-the-art algorithm viz. Colour Block Matching 3D (C-BM3D) filter, which achieves the first rank among 15 algorithms reported in the most recent literature, is used for the restoration of noisy texture benchmark images. Image restoration performance metrics used are peak signal to noise ratio (PSNR) and structural similarity index (SSIM). © 2020 IEEE.