GitHub

Image Denoising with U-Net

Worst case improvement: Bright/colourful images improved from 17-18 dB (baseline) to 23+ dB through targeted preprocessing augmentation — a +6 dB gain on previously failing cases.
TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and data preprocessing. If you’ve ever built a predictive model, worked on a ...
IEEE

Neural Network-Based Image Captioning

Abstract: Image captioning integrates computer vision and natural language processing to enable AI to generate descriptive text for visual content. This approach combines Convolutional Neural Networks ...
IEEE

Multiple-Frame Quality Compensation Encoding Method Based on H.266 / VVC

Abstract: This research focuses on the multi-frame quality compensation coding method based on H.266/VVC. By leveraging technologies such as optical flow algorithms and convolutional neural networks, ...
GitHub

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors

Official PyTorch Implementation of Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors - Dmitro72/Magic12345 ...