Analysis of DCT and FAST DCT using soft core processor

Mutgekar, Meghanan Bhairu; Bhaskar, P. C.

doi:10.1109/icoei.2019.8862715

Cited by 6 publications

(1 citation statement)

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“…R(u) represents a regularization term representing prior information of an image. The current CS recovery algorithms explore the prior knowledge that a natural image is sparse in some domains, such as discrete cosine transform (DCT) [7], wavelets [8], and gradient-domain utilized by total variation (TV) model [9,10]. Image sparse representation is a key factor to affect image reconstruction quality.…”

Section: Introductionmentioning

confidence: 99%

Hybrid High-Order and Fractional-Order Total Variation with Nonlocal Regularization for Compressive Sensing Image Reconstruction

et al. 2021

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Total variation often yields staircase artifacts in the smooth region of the image reconstruction. This paper proposes a hybrid high-order and fractional-order total variation with nonlocal regularization algorithm. The nonlocal means regularization is introduced to describe image structural prior information. By selecting appropriate weights in the fractional-order and high-order total variation coefficients, the proposed algorithm makes the fractional-order and the high-order total variation complement each other on image reconstruction. It can solve the problem of non-smooth in smooth areas when fractional-order total variation can enhance image edges and textures. In addition, it also addresses high-order total variation alleviates the staircase artifact produced by traditional total variation, still smooth the details of the image and the effect is not ideal. Meanwhile, the proposed algorithm suppresses painting-like effects caused by nonlocal means regularization. The Lagrange multiplier method and the alternating direction multipliers method are used to solve the regularization problem. By comparing with several state-of-the-art reconstruction algorithms, the proposed algorithm is more efficient. It does not only yield higher peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) but also retain abundant details and textures efficiently. When the measurement rate is 0.1, the gains of PSNR and SSIM are up to 1.896 dB and 0.048 dB respectively compared with total variation with nonlocal regularization (TV-NLR).

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