Comparative analysis of FISTA and inertial Tseng algorithm for enhanced image restoration in prostate cancer imaging

Abstract

The Inertial Tseng Algorithm (ITA) and the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) are well-established methods that provide effective ways to approximate zeros of the sum of monotone operators. In this study, we applied both ITA and FISTA in the restoration process of prostate cancer and osteosarcoma images that were degraded with known blur functions and additive noise. The test images comprised osteosarcoma tumours I and II, prostate fused, and prostate MRI pathology. Both algorithms incorporated the TV-regularizer and [Formula: see text]-regularizer. Numerical simulations revealed that the ITA method consistently outperforms the FISTA method in terms of image quality, despite FISTA's computational efficiency advantage. Additionally, the study found that, for both methods, images restored using the TV-regularizer exhibit higher quality compared to those restored using the [Formula: see text]-regularizer. Overall, the study revealed the effectiveness of the algorithms employed and highlight the significance of integrating mathematical models with well established mechanism in medical image restoration.

Keywords

• Medical image restoration
• image deblurring
• Tseng algorithm
• prostate cancer
• osteosarcoma
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