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README.md

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+# Jingwen Wang s5820023 ML Project
+
+## Introduction
+
+This project aims to develop a machine learning tool that generates PBR material maps from single photographs, addressing the lighting contamination issue in existing scanned material libraries. The focus will be on a single material category, with the first stage targeting either extracting base color by removing lighting effects, or implementing photo super-resolution.
+
+## Main Approach
+
+**Option 1:** Pixel classification - classify pixels into diffuse, specular highlight, and shadow regions to extract base color
+
+**Option 2:** Super-resolution - upscale low-resolution photos to 2K/4K quality
+
+Feedback needed on which approach is more suitable for the first stage.
+
+## Key Datasets
+
+- Segmentation: UCI Image Segmentation Dataset
+- Super-resolution: DIV2K, Urban100
+
+## Reading Material
+
+- Minaee, Shervin, et al. Image Segmentation Using Deep Learning: A Survey. arXiv, 2020.
+- Wang, Zhihao, et al. Deep Learning for Image Super-Resolution: A Survey. arXiv, 2020.