Application of Image Inpainting in Drug and Alcohol Addiction Research Using Repeated Convolution Method
Author(s): Jini P* and Rajkumar KK
Abstract
Image inpainting is a promising but challenging approach that fills in huge free-form empty areas in images. Most of the recent papers concentrate on splitting masked image into 2 matrices of valid and invalid elements which makes the system more complex. This paper proposes a novel algorithm named ReConv which uses a repeated standard convolution operation which treats valid and invalid elements of an image in the same manner. The outcomes of our suggested method, ReConv, shows that, in comparison to earlier approaches, our system produces outputs that are more adaptable with good quality for real world applications. In the context of drug and alcohol addiction treatment and research, this technology offers several unique and emerging applications like Therapeutic Visual Stimuli Modification. Inpainting techniques can fill in missing data in addiction-related images, such as damaged MRI scans or incomplete survey responses, enhancing the predictive capacity of machine learning models used in addiction research. An extensive comparison study on 2 types of datasets validates our method. The effectiveness of the suggested strategy was evaluated using different measures such as PSNR, SSIM and FID. The results show that our recommended approach excels in performance compared to the existing modern methods.