Adversarial Artifact Removal: A GAN-Based Framework for Enhanced Image Quality and Reliability

Artifacts in images are significant challenges in many fields and degrade the quality and reliability of data analysis. Existing methods for artifact removal face a problem of complex and diverse artifacts, limiting their effectiveness. Traditional techniques for removing artifacts have a problem with low adaptability to different types of artifacts and can sometimes delete important features of an image. The artifact removal method takes the concept of GANs into use to remove artifacts within the adversarial training model from images. It rectifies the issues stated previously through the proposed approach based on training a GAN. The G learns artifact-free versions of the images and the D tells them clean or corrupted ones while the G continues its progression towards artifact removal guided by D. Once it was trained, G had completely removed artifacts in novel unseen images. Results prove the superiority of this proposed artifact removal compared with existing approaches across a spectrum of datasets including different forms of artifacts: noise compression, and occlusion results of removing such different kinds of artifacts without sacrificing much the image quality along with preserving other critical features that need removal. Adversarial removal artifacts can be taken as one such error value 112.31 solution in making an improved version of enhancing quality as well as reliability of the image while making applications over the range.