From 153950a958db7761b00dd1967b9e7b85e2049e8f Mon Sep 17 00:00:00 2001 From: towzeur Date: Mon, 4 Mar 2024 11:45:48 +0100 Subject: [PATCH] Update meta tags and add banner image --- index.html | 36 +++++++++++++++++++++--------------- static/images/banner.png | Bin 0 -> 445290 bytes 2 files changed, 21 insertions(+), 15 deletions(-) create mode 100644 static/images/banner.png diff --git a/index.html b/index.html index 56d1b5a..e2335fa 100644 --- a/index.html +++ b/index.html @@ -5,23 +5,25 @@ - - - - + + + + - + - + - - + + - - + + - + @@ -238,9 +240,12 @@

Proposed method

promise in artifact removal and structure preservation but face limitations due to constrained receptive fields, leading to suboptimal results. However, these methods are computationally efficient. -
  • Deep unrolling algorithms, such as the RegFormer algorithm, have been introduced as an iterative reconstruction methods. - However, they face issues such as slow convergence and high computational costs. Consequently, there is a need to explore more efficient - alternatives due to the difficulties in capturing long-range dependencies and the growing computational demands of modern neural networks. +
  • Deep unrolling algorithms, such as the RegFormer algorithm, have been introduced as an iterative + reconstruction methods. + However, they face issues such as slow convergence and high computational costs. Consequently, there is a + need to explore more efficient + alternatives due to the difficulties in capturing long-range dependencies and the growing computational + demands of modern neural networks.
  • The paper introduces a second-order unrolling network called QN-Mixer, which employs a latent BFGS algorithm to approximate the inverse Hessian matrix with a deep-net learned regularization term. @@ -353,7 +358,8 @@

    BibTeX

    Acknowledgements

    - This work was granted access to the HPC resources of IDRIS under the allocation 2021-[AD011012741] provided by GENCI and supported by DIM Math Innov funding. + This work was granted access to the HPC resources of IDRIS under the allocation 2021-[AD011012741] provided by + GENCI and supported by DIM Math Innov funding.

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