lab-color-space

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.

Practical ComfyUI nodes: workflow screenshots (4x), Load Checkpoint/Diffusion with name clean, CLIP Text Encode with offload, Save Image with Prompt metadata, HDR Effects with LAB Adjust, Image Difference Checker. Supports Nodes 2.0 and V3 schema (v2.0.0).

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.

End-to-end automated image colorization using U-Net with skip connections in LAB color space — PyTorch, Gradio, 28.26 dB PSNR

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

Camera locking and LAB color calibration tools for MaixCAM2 (MaixPy): AE/AWB locking, white balance tuning, threshold sampling.

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

Implementing and comparing CGAN and WGAN-GP for grayscale image colorization using U-Net and PatchGAN in L*a*b color space, with experiments on ImageNet and MS-COCO.

Skin tone analysis pipeline using OpenCV and MediaPipe Face Mesh. Landmark based skin region sampling with white balance and CLAHE illumination correction, then classification using LAB color space, ITA angle, melanin index and Fitzpatrick type, with undertone detection.

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.

An automated pipeline that processes glyph images to extract quantitative metrics and semantic visual features, producing rich, searchable data objects optimized for storage & retrieval