Deep learning grayscale (black and white) to color image conversion using U-Net autoencoder architecture in PyTorch. Converts grayscale images to RGB using LAB color space prediction with encoder-decoder neural networks.

A Python-based image color identification tool using OpenCV and the LAB (CIELAB) color space for enhanced perceptual accuracy. This project allows users to click on any part of an image to detect the closest color match from a predefined color dataset.

PyTorch U‑Net image colorization (LAB color space) — trained on COCO, includes evaluation (PSNR/SSIM/RMSE/SNR) and a Gradio demo.

OpenCV ile HSV ve LAB renk uzaylarını kullanan hibrit kırmızı nesne tespiti sistemi. Otonom sistemler ve İHA projeleri için optimize edilmiştir. (Hybrid red object detection using HSV & LAB color spaces.)

NLP-driven lipstick recommender that turns free-text preferences into color-constrained rankings.

Hierarchical image colorization model combining a Swin Transformer encoder with an EMA-based VQ-VAE bottleneck and a residual decoder. Learns discrete color representations and produces realistic, perceptually consistent colorizations

Deep learning video colorization using MobileNetV2 U-Net, LAB color space, OpenCV, TensorFlow, and FFmpeg.