nihui/waifu2x-ncnn-vulkan

Features

• Convolutional Neural Network Upscalers - Provides AI-driven super-resolution using convolutional neural networks to increase image resolution without blur.
• GPU Acceleration - Accelerates heavy image enhancement tasks by offloading neural network inference to the GPU.
• Image Super Resolution Models - Implements deep learning architectures to increase image resolution and reconstruct fine details.
• Inference Frameworks - Utilizes the NCNN framework for efficient neural network inference on local hardware.
• GPU-Accelerated Image Denoisers - Cleans visual artifacts and noise from images using high-performance graphics card acceleration.
• Image Denoising - Removes visual artifacts from images by applying configurable deep-learning denoising strengths.
• AI Upscaling - Increases image dimensions using neural networks to improve visual clarity and restore fine detail.
• Image Noise Reduction - Removes visual artifacts and grain from images using deep learning-based noise reduction.
• Neural Image Processors - Provides an image processing implementation based on the NCNN framework for efficient local inference.
• Vulkan-Based Upscalers - Uses Vulkan for hardware-accelerated image scaling and noise reduction on GPUs.
• Vulkan API Support - Uses the Vulkan API to execute neural network computations across diverse graphics hardware for acceleration.
• VRAM Tiling Optimizations - Balances processing speed and graphics memory consumption by adjusting tile sizes and thread counts.
• Image Tiling - Employs tiled image processing to split large images into smaller blocks to prevent GPU memory overflow.
• Image Processing Batchers - Provides batch processing capabilities to automate the conversion of multiple image files across directories.
• Configurable Denoising Strengths - Allows adjusting internal neural network weights via configuration flags to balance artifact removal and detail.

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