Enhanced Fast Iterative Shrinkage Thresholding Algorithm For Linear Inverse Problem

TL;DR

This paper introduces EFISTA, an enhanced algorithm for linear inverse problems like image deblurring, which accelerates convergence and improves image quality, especially under high noise conditions.

Contribution

The paper proposes EFISTA, a novel accelerated algorithm using weighted least squares and scaled regularization to improve speed and accuracy in linear inverse problem solutions.

Findings

EFISTA achieves faster convergence than previous methods.

EFISTA improves PSNR in noisy image deblurring.

EFISTA is effective for various linear inverse problems.

Abstract

The linear inverse problem emerges from various real-world applications such as Image deblurring, inpainting, etc., which are still thrust research areas for image quality improvement. In this paper, we have introduced a new algorithm called the Enhanced fast iterative shrinkage thresholding algorithm (EFISTA) for linear inverse problems. This algorithm uses a weighted least square term and a scaled version of the regularization parameter to accelerate the objective function minimization. The image deblurring simulation results show that EFISTA has a superior execution speed, with an improved performance than its predecessors in terms of peak-signal-to-noise ratio (PSNR), particularly at a high noise level. With these motivating results, we can say that the proposed EFISTA can also be helpful for other linear inverse problems to improve the reconstruction speed and handle noise effectively.