Deblurring Images by Huber Lasso

We propose a proximal-gradient deblurring method that replaces the least-squares data term in FISTA with the Huber loss and augments it with momentum acceleration. The resulting algorithms, called HISTA and FHISTA, combine robust Huber fidelity with an absolute value sparsity penalty and Nesterov-style extrapolation. Experiments on twelve benchmark images blurred by a Gaussian kernel and contaminated with Gaussian noise show that FHISTA improves PSNR by roughly five decibels over classical ISTA. The method is easy to implement, uses a modest number of hyper-parameters, and demonstrates strong resilience to outliers.

• Publication year

  2025

• Venue

  International Conference on Image Processing Theory Tools and Applications

• Publication date

  2025-10-13

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1109/IPTA66025.2025.11222024
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• A Huber Function based Restoration Algorithm for Astronomy Image Compression

  2021cited by this paper
• Decentralized Accelerated Proximal Gradient Descent

  2020cited by this paper
• Minimizers of Sparsity Regularized Huber Loss Function

  2020cited by this paper
• First-Order Methods in Optimization

  2017cited by this paper
• Fast Parallel MR Image Reconstruction via B1-Based, Adaptive Restart, Iterative Soft Thresholding Algorithms (BARISTA)

  2015cited by this paper
• Efficient Algorithms for Robust Recovery of Images From Compressed Data

  2012cited by this paper
• Regularization Paths for Generalized Linear Models via Coordinate Descent.

  2010cited by this paper
• Smoothing proximal gradient method for general structured sparse regression

  2010cited by this paper
• Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization

  2009cited by this paper
• NESTA: A Fast and Accurate First-Order Method for Sparse Recovery

  2009cited by this paper
• Deblurring Images: Matrices, Spectra, and Filtering (Fundamentals of Algorithms 3) (Fundamentals of Algorithms)

  2006cited by this paper
• An iterative thresholding algorithm for linear inverse problems with a sparsity constraint

  2003cited by this paper
• On robust estimation of the location parameter

  1980cited by this paper

• No citing papers are available for this paper.