Image Inpainting is the field that reconstructs the missing regions with high-quality image restoration. The image restoration addresses the loss of conditional details and distortion of visual quality in damaged images. In this paper, a Scale-Variant Learning (SVL) model is presented that combines super-resolution and deep image inpainting for the restoration of missing or damaged regions, resulting in increased image clarity and high resolution. The proposed SVL consists of a super-resolution network that first recovers high-frequency details from low-quality input and an inpainting module that fills in missing regions by leveraging both contextual and structural information. To generate photo-realistic output, a multi-scale feature fusion, attention mechanisms, and adversarial learning are used to enable the model learns the global semantics and local texture consistency. The pre-trained model DeepFill v1, with transfer learning applied to large-scale high-resolution datasets (CelebA-HQ and Places2), shows competitive performance compared to existing techniques in both quantitative evaluations and visual appearance. This pre-trained model fills the gap between missing content and accurate regions, from image restoration and editing to historical image repair and computer vision tasks that require fine-grained representation.