Authors:
Farhan Bashar
and
Mahmoud R. El-Sakka
Affiliation:
The University of Western Ontario, Canada
Keyword(s):
Image Denoising, Additive White Gaussian Noise, BM3D, Adaptive Threshold, Classification, Random Forest Classifier, PSNR, SSIM.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
Abstract:
Block Matching and 3D Filtering (BM3D) is considered to be the current state-of-art algorithm for additive image denoising. But this algorithm uses a fixed hard threshold value to attenuate noise from a 3D block. Experiment shows that this fixed hard thresholding deteriorates the performance of BM3D because it does not consider the context of corresponding blocks. We propose a learning based adaptive hard thresholding method to solve this problem and found excellent improvement over the original BM3D. Also, BM3D algorithm requires as an input the value of noise level in the input image. But in real life it is not practical to pass as an input the noise level of an image to the algorithm. We also added noise level estimation method in our algorithm without degrading the performance. Experimental results demonstrate that our proposed algorithm outperforms BM3D in both objective and subjective fidelity criteria.
