  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.
