  Quantum Image Processing (QIP) is a field that aims to utilize the benefits
of quantum computing for manipulating and analyzing images. However, QIP faces
two challenges: the limitation of qubits and the presence of noise in a quantum
machine. In this research we propose a novel approach to address the issue of
noise in QIP. By training and employing a machine learning model that
identifies and corrects the noise in quantum processed images, we can
compensate for the noisiness caused by the machine and retrieve a processing
result similar to that performed by a classical computer with higher
efficiency. The model is trained by learning a dataset consisting of both
existing processed images and quantum processed images from open access
datasets. This model will be capable of providing us with the confidence level
for each pixel and its potential original value. To assess the model's accuracy
in compensating for loss and decoherence in QIP, we evaluate it using three
metrics: Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM),
and Mean Opinion Score (MOS). Additionally, we discuss the applicability of our
model across domains well as its cost effectiveness compared to alternative
methods.
