Gaussian De-Noising Techniques
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Image de-noising is the elimination of noise from digital images where noise is any undesired information that corrupts an image. De-nosing is achieved through various filtering techniques that not only enhance the image but also keeps all its important details. De-noising filters are categorized into linear and non-linear techniques. You will see the most popular image quality assessment metrics such as PNSR, MSE and SSIM. In addition, highlights on recent de-noising literature since 2010 up to 2014. The technical part of this book presents applying Gaussian de-noising algorithms in spatial d...
Image de-noising is the elimination of noise from digital images where noise is any undesired information that corrupts an image. De-nosing is achieved through various filtering techniques that not only enhance the image but also keeps all its important details. De-noising filters are categorized into linear and non-linear techniques. You will see the most popular image quality assessment metrics such as PNSR, MSE and SSIM. In addition, highlights on recent de-noising literature since 2010 up to 2014. The technical part of this book presents applying Gaussian de-noising algorithms in spatial domain for medical images. Actually, five de-noising techniques (Geometric, Harmonic, Alpha-trimmed, midpoint and local noise reduction filters) are developed on gray scale medical images which are corrupted by additive Gaussian noise with mean = 0, variance = 1000. Analysis has been done for the de-nosing techniques in terms of MSE and PSNR for image quality assessment and time complexity for performance assessment.
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