Neural-network-based Impulse Noise Removal Using Group-based Weighted Couple Sparse Representation

• Yongwoo Lee,
• Toan Duc Bui,
• Jitae Shin,
• Byung Tae Oh,

Vol. 12, No. 8, August 30, 2018
10.3837/tiis.2018.08.018 , Download Paper (Free):

• Neural Network
• noise detector
• Denoising
• impulse noise
• sparse representation

Abstract

In this paper, we propose a novel method to recover images corrupted by impulse noise. The proposed method uses two stages: noise detection and filtering. In the first stage, we use pixel values, rank-ordered logarithmic difference values, and median values to train a neural-network-based impulse noise detector. After training, we apply the network to detect noisy pixels in images. In the next stage, we use group-based weighted couple sparse representation to filter the noisy pixels. During this second stage, conventional methods generally use only clean pixels to recover corrupted pixels, which can yield unsuccessful dictionary learning if the noise density is high and the number of useful clean pixels is inadequate. Therefore, we use reconstructed pixels to balance the deficiency. Experimental results show that the proposed noise detector has better performance than the conventional noise detectors. Also, with the information of noisy pixel location, the proposed impulse-noise removal method performs better than the conventional methods, through the recovered images resulting in better quality.

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