ABSTRACT
Art designs exhibit different principles, textures, color combinations, and creative skills for vivid thinking visualizations. Art exhibits are far from ages, periods, and creators finding their digital patterns in recent years for resurrection. Degraded periodic artworks are digitally handled for reviving their legacy using digital image processing. This article introduces Textural Restoration Technique (TRT) using Deep Feature Processing (DFP) to augment such innovations. The proposed technique analyses the tampered image for its textures, and available features are extracted. The textures are expected to be sequential based on gradient distribution; the missing gradients are identified from the available features near the region of interest (ROI). The ROI is marked by combining missing and available features from which textural edges are sketched. In this process, recurrent learning is employed for verifying the gradient substitutions for even textures. The texture patterns are classified using high and low accuracy features exhibited between two successive ROIs. First, the learning model is trained using gradient distribution accuracy pursued by the texture completion edge. The second training is pursued by the first distribution, achieving the maximum restoration. The filled features and their gradient positions are marked by moving the ROIs for distinguishing textures. The restoration ratio is computed with high accuracy based on the filled edges.
