Novel Conjugate Gradient Methods for Image Restoration

Abstract

The image restoration problem can be solved with the right techniques and tools. Although the quality of images may be lowered throughout the data collecting procedure, there are ways to do so. Clarity may be enhanced as a result of the procedure. It is the goal of image restoration to restore the original image from the damaged data. Here are some examples of applications in forensic research, picture restoration, medical imaging, astronomical imaging, and more. It can be advantageous to enhance image quality as much as possible, which justifies the expense and difficulty of the restoration methods.  The conjugate gradient method is a numerical optimization technique that places emphasis on the conjugate of the coefficient in order to determine the optimal solution. The current study introduces a novel quadratic function-based coefficient conjugate gradient methodology for the removal of impulse noise from pictures. Global convergence has been shown to occur with the suggested method. The numerical results indicate that the proposed methods have high efficiency and global convergence in image restoration problems compared to the Fletcher-Reevess (FR) method.