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Title: Image denoising: who is the best?
Authors: Toh, Sheng Rong
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Toh, S. R. (2022). Image denoising: who is the best?. Final Year Project (FYP), Nanyang Technological University, Singapore.
Abstract: While high-quality images are often desirable, image noise is often inevitable. With that said, many image denoising methods have been developed over the years, and we want to compare and find the best image denoising method available for real-world images. We will be implementing traditional methods such as the non-local means (NLM) and block-matching and 3D filtering (BM3D), and deep learning models such as autoencoder, denoising convolutional neural network (DnCNN) and real image denoising with feature attention (RIDNet) for comparison. 160 coloured clean-noisy image pairs will be used in this experiment. Through this experiment, we have found that RIDNet is the most effective image denoising method out of the 5 mentioned above.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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