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Applying Compressive Sensing Techniques for Image Enhancement

Ujan, Sahar | 2016

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 48781 (52)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Ghorshi, Mohammad Ali
  7. Abstract:
  8. This thesis proposes a novel method for enhancing the image signal based on compressed sensing. Compressed sensing, as a new rapidly growing research field, promises to effectively recover a sparse signal at the rate of below Nyquist rate. This revolutionary technology strongly relies on the sparsity of the signal and incoherency between sensing basis and representation basis. Exact recovery of a sparse signal will be occurred in a situation that the signal of interest sensed randomly and the measurements are also taken based on sparsity level and log factor of the signal dimension. In this research, compressed sensing method is proposed to reduce the noise and reconstruct the image signal. Noise reduction and image reconstruction are formulated in the theoretical framework of compressed sensing using Basis Pursuit (BP) and Compressive Sampling Matching Pursuit (CoSaMP) algorithm when random measurement matrix is utilized to acquire the data. Ultimately, it is demonstrated that the proposed method can not perfectly recover the image signal. The reason for this failure is that natural images do not have an exactly sparse representation in any known basis such as Discrete Cosine Transform (DCT), Wavelet, Curvelet, etc. In this thesis we take a complementary approach for enhancing the performance of CS recovery with non-sparse signals. Therefore, we use a new designed CS recovery framework, called de-noising-based approximate message passing (DAMP). This method uses a de-noising algorithm to recover signals from compressive measurements. For de-noising purpose the Non-local means (NLM), Bayesian least squares Gaussian Scale mixtures (BLS-GSM) and Block matching 3D collaborative filter (BM3D) algorithms have been used
  9. Keywords:
  10. Image Enhancement ; Basis Pursuit Regularisation (BPR)Algorithm ; Compressive Sensing ; Compressive Sampling ; Sparsifying

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