Evolutionary Fuzzy Block-Matching-Based Camera Raw Image Denoising

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)


An evolutionary fuzzy block-matching-based image denoising algorithm is proposed to remove noise from a camera raw image. Recently, a variance stabilization transform is widely used to stabilize the noise variance, so that a Gaussian denoising algorithm can be used to remove the signal-dependent noise in camera sensors. However, in the stabilized domain, the existed denoising algorithm may blur too much detail. To provide a better estimate of the noise-free signal, a new block-matching approach is proposed to find similar blocks by the use of a type-2 fuzzy logic system (FLS). Then, these similar blocks are averaged with the weightings which are determined by the FLS. Finally, an efficient differential evolution is used to further improve the performance of the proposed denoising algorithm. The experimental results show that the proposed denoising algorithm effectively improves the performance of image denoising. Furthermore, the average performance of the proposed method is better than those of two state-of-the-art image denoising algorithms in subjective and objective measures.

頁(從 - 到)2862-2871
期刊IEEE Transactions on Cybernetics
出版狀態Published - 2017 九月

All Science Journal Classification (ASJC) codes

  • 軟體
  • 控制與系統工程
  • 資訊系統
  • 人機介面
  • 電腦科學應用
  • 電氣與電子工程


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