Using deep learning to generate wood textures from pixels to planks

ABSTRACT The research titled "Pixels to Planks: Deep Learning for Wood Texture" explores the application of deep learning in analyzing, classifying, and synthesizing wood textures from digital images. Traditional wood texture identification methods are largely manual and subjective, relying on expert knowledge and physical inspection. These methods are time consuming, inconsistent, and limited in scalability. The proposed system uses a Convolution Neural Network (CNN) integrated with texture enhancement and feature extraction layers to automatically recognize and generate realistic wood patterns. The model is trained on a curated dataset of high-resolution wood images spanning different grain types, colors, and anomalies (e.g., knots, decay). The system outperforms traditional machine vision approaches in accuracy and adaptability, with potential applications in forestry, furniture design, quality control, and augmented reality. This approach brings several advantages by enhancing the speed and reliability of wood analysis. It can be applied across multiple domains like digital design, furniture manufacturing, virtual simulations, and quality control processes. Additionally, by digitizing and preserving unique wood patterns, the project encourages sustainable practices by reducing the need to harvest real wood. Overall, this work demonstrates how artificial intelligence can modernize traditional industries, making them more efficient and environmentally conscious. Keywords: Computer Vision, Image Processing, Feature Extraction, Neural Networks, Dataset Training, Paflern Recognition, Generative Models.