An Enhanced Integrated Model for Image Inpainting Using Gated Convolution Spectral Normalized SN-Patch Generative Adversarial Networks

Image inpainting is the process of reconstructing missing or damaged regions in an image and is an important task in computer vision applications for restoration and enhancement. However, repair algorithms are often sensitive to noise and yield suboptimal results. To address this challenge, a new integrated two-stage framework is introduced to improve the performance of image inpainting. In the first stage, an effective Noise2Void denoising is applied to learn meaningful representations of image patches and effectively denoise the input image. The proposed N2V model considers the structural links between pixels and retains contextual information at the same time, while suppressing noise. In the 2nd stage, an advanced enhanced DeepFill inpainting model employing deep neural networks is applied. Experimental results showed that the method proposed will outperform traditional repair methods. The denoising step tunes the accuracy of reconstructing missing areas, and greatly improves the quality of inpainting. Applied on huge benchmark datasets, the performance is evaluated and demonstrated that N2V integrated with DeepFill outperforms individual inpainting techniques. Furthermore, we carry out an ablation study to evaluate the contribution of each constituent part of our proposed framework. This outcome underscores the complementary nature of the denoising and repair stages and points to the need for noise control before repairs. In general, our technique provides a strong and effective approach to image restoration tasks and allows for improving inpainting methods under real-world conditions.