Images have been a significant medium for information analysis in recent years. However, Occasionally, the collected image may suffer from poor light and become hazy due to technological restrictions imposed by the surrounding environment. This process may result in a decline in visual quality. Even though reducing deterioration is an essential step in image restoration, image processing researchers still encounter challenges during the restoration process. Image restoration is the process of restoring a damaged image to its most authentic state. Degradation can be caused by motion blur, Gaussian blur, noise, camera mismatch, uneven lighting, and photos with low contrast. To enhance image quality, the suggested approach incorporates the creation of a novel amplitude filter mask based on the pupil function methodology for pictures of point objects. By MATLAB, two filters were created to improve low-contrast and fuzzy photos. The performance of this algorithm has been computed based on the signal-to-noise ratio (PSNR), Enhancement Measure Estimation (EME), image entropy, depending on this statistical improvement measures, the results show that the apodization parameter β plays an important role of enhancing the degraded images.