Image inpainting by anisotropic gradient estimation

Automatic restoration of damaged or missing pixels is a key problem in image reconstruction for various applications such as retouching, image restoration, image coding, and computer vision. This paper presents a novel approach for reconstructing texture and edge regions, focusing on achieving fine detail in image completion. The proposed method employs spatial reconstruction based on a geometric model, incorporating contour and exemplar-based texture analysis. We propose a technique for restoring object boundaries in images by constructing composite curves using cubic splines and anisotropic gradients. The shape-dependent gradients utilize the distinct forms in the structural pattern to encode both textural and contour information. Additionally, we search for similar patches, fuse them, and apply a deep neural network. We evaluate our model end-to-end on publicly available datasets, demonstrating that it outperforms current state-of-the-art techniques both quantitatively and qualitatively.