| Summary: | 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.
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