SkillAgentSearch skills...

LLE

Low Light Enhancement Deeplearning Model Inference API

GenerateConvertImprove

Install / Use

/learn @gellston/LLE
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

<div align="center"> <img src="https://raw.githubusercontent.com/gellston/LLE/main/icon.png" alt="LLE Icon" width="140" />

LLE (Low Light Enhancement)

AI-based Low-Light Enhancement inference API for <b>Windows x64</b>.

<!-- Native (Cu118) --> <a href="https://www.nuget.org/packages/LLE.Native.Cu118"> <img src="https://img.shields.io/nuget/v/LLE.Native.Cu118.svg?style=for-the-badge&logo=nuget&label=NuGet%20Native%20Cu118" /> </a> <img src="https://img.shields.io/badge/CUDA-11.8-76B900?style=for-the-badge&logo=nvidia" /> <br/> <!-- Managed (Cuda118) --> <a href="https://www.nuget.org/packages/LLE.Managed.Cu118"> <img src="https://img.shields.io/nuget/v/LLE.Managed.Cu118.svg?style=for-the-badge&logo=nuget&label=NuGet%20Managed%20Cuda118" /> </a> <img src="https://img.shields.io/badge/CUDA-11.8-76B900?style=for-the-badge&logo=nvidia" /> <br/> <img src="https://img.shields.io/badge/C%2B%2B-Used-00599C?style=for-the-badge&logo=c%2B%2B" /> <img src="https://img.shields.io/badge/C%2B%2B%2FCLI-Used-512BD4?style=for-the-badge" /> <img src="https://img.shields.io/badge/C%23-Used-512BD4?style=for-the-badge&logo=csharp" /> <img src="https://img.shields.io/badge/Python-Model%20Training-3776AB?style=for-the-badge&logo=python" /> </div>

Overview

LLE is a low-light image enhancement library that provides an inference API for an AI model trained in Python.

NuGet Packages (Native vs Managed)

  • LLE.Native.Cu118 (C++ / native)
    Native runtime + C++ API for Windows x64. Use this if you want to call LLE directly from C++.

  • LLE.Managed.Cuda118 (C# / .NET)
    A managed wrapper (C++/CLI) around the native runtime for a smoother .NET experience on Windows x64.

Both packages target Windows x64. GPU inference requires a compatible NVIDIA GPU environment (see below).

Sample Result (Before / After)

A quick visual comparison using the bundled sample images.

| Low-light input | Enhanced output | | --- | --- | | low_61 | enhanced_low_61 |

If images don’t render on NuGet.org, switch these URLs to raw links:

  • https://raw.githubusercontent.com/gellston/LLE/main/samples/low_61.png
  • https://raw.githubusercontent.com/gellston/LLE/main/samples/enhanced_low_61.png

Model Support (Now / Next)

  • Current: Low-light enhancement inference (native runtime)
  • Planned: Support improved low-light enhancement models over time (quality / speed / size trade-offs), and expand model options as the project evolves.

LLE is not a one-off release. Model quality and available variants may improve through updates.

Training Scripts

  • Training scripts used for model development are available here:
    • https://github.com/gellston/LLE/tree/main/python

Dataset

  • This project used the LOLI-Street low-light street image dataset:
    • https://www.kaggle.com/datasets/tanvirnwu/loli-street-low-light-image-enhancement-of-street

Platform

  • ✅ Windows x64 only
    • Even if you use C# or C++, this library only works on Windows x64.

Runtime (CPU / CUDA)

CPU

  • CPU inference: no special runtime constraints (beyond standard Windows x64 requirements).

CUDA (GPU)

  • CUDA inference requires an NVIDIA GPU + driver.
  • You must install CUDA 11.8 on the target machine.
  • You must install cuDNN 8.5.0.96 (CUDA 11.x build) on the target machine.
  • This package does not bundle the NVIDIA CUDA / cuDNN redistributable DLLs.
    • Make sure CUDA/cuDNN DLLs are discoverable at runtime (e.g., in PATH or alongside your app).

If CUDA inference fails to load (e.g., DLL not found / entry point not found), the most common causes are:

  • NVIDIA driver is outdated/incompatible
  • CUDA/cuDNN versions do not match (CUDA 11.8 + cuDNN 8.5.0.96)
  • CUDA/cuDNN DLLs are not on PATH (or not deployed next to the executable)

CUDA / cuDNN Installation Links

Note: NVIDIA downloads may require an NVIDIA Developer account login.

  • CUDA Toolkit 11.8 (Windows x86_64)
    https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Windows&target_arch=x86_64

  • cuDNN v8.5.0.96 (CUDA 11.x, Windows x86_64 zip)
    Option A (recommended entry): cuDNN Archive (pick v8.5.0 for CUDA 11.x → Windows x86_64 (Zip))
    https://developer.nvidia.com/rdp/cudnn-archive

    Option B (direct file; will redirect to NVIDIA login):
    https://developer.nvidia.com/compute/cudnn/secure/8.5.0/local_installers/11.7/cudnn-windows-x86_64-8.5.0.96_cuda11-archive.zip

CPU + CUDA Mixed Usage (Important)

  • LLE.Native.Cu118 and LLE.Managed.Cu118 can be used in a mixed mode:
    • You can run CPU inference regardless of CUDA availability.
    • To run CUDA inference, you must have a compatible NVIDIA driver + CUDA 11.8 + cuDNN 8.5.0.96 installed/configured.
    • This enables CPU fallback or choosing CPU/CUDA per workload.

Development Environment

  • Visual Studio 2026

Runtime Dependency (Required)

This library requires a separate redistribution package to run (native runtime DLLs, etc.). Download and install the redistribution package before using LLE.

  • Microsoft Visual C++ Redistributable (Latest Supported): https://learn.microsoft.com/ko-kr/cpp/windows/latest-supported-vc-redist?view=msvc-170

NuGet Packages

LLE is not a “single one-off release”. The NuGet packages can be updated over time (bug fixes, performance improvements, new runtime variants, model upgrades).

Current / planned package list:

  • LLE.Native.Cu118 (Windows x64, native runtime, CPU, requires CUDA 11.8 + cuDNN 8.5.0.96 for GPU)
    https://www.nuget.org/packages/LLE.Native.Cu118
  • LLE.Managed.Cuda118 (Windows x64, managed wrapper for .NET / C#; uses the native runtime under the hood)
    https://www.nuget.org/packages/LLE.Managed.Cuda118

The list may expand (e.g., different CUDA versions) and existing packages may receive updates.

Installation

C++ (native)

Package Manager

Install-Package LLE.Native.Cu118

.NET CLI

dotnet add package LLE.Native.Cu118

.NET / C# (managed wrapper)

Package Manager

Install-Package LLE.Managed.Cuda118

.NET CLI

dotnet add package LLE.Managed.Cuda118

Usage in C++

#include <lle/memoryPool.h>
#include <lle/image.h>
#include <lle/lle.h>
#include <iostream>

int main()
{
    try {
        // create lle instance
        auto lle = lleapi::v1::lle::create();
        // load Zero-DCE++ model on CPU
        // (also supports loading an ONNX model from a file path)
        lle->setup(lleapi::v1::dlType::zeroDCE, lleapi::v1::device::cpu);
        // load color image
        auto input = lleapi::v1::image::imread(
            "C://github//dataset//lol_dataset//our485//low//low_15.png",
            lleapi::v1::colorType::color
        );
        // predict
        auto output = lle->predict(input);
        // save result image
        lleapi::v1::image::imwrite(
            "C://github//LLE//LLE//x64//Debug//result1.jpg",
            output
        );
        // cleanup internal instance
        lle->shutdown();
    }
    catch (std::exception ex) {
        std::cout << ex.what() << std::endl;
    }
}

Usage in C#

namespace ManagedTest
{
    internal class Program
    {
        static void Main(string[] args)
        {
            try
            {
                //// Create LLE Instance
                var lle = LLEAPI.V1.LLE.Create();
                //// load zerodce model and load on cpu
                //// its also support onnx model load from path
                lle.Setup(LLEAPI.V1.DlType.ZeroDCE, LLEAPI.V1.Device.Cpu);
                //// load color image
                var input = LLEAPI.V1.Image.Imread(
                    "C://github//dataset//lol_dataset//our485//low//low_15.png", 
                    LLEAPI.V1.ColorType.Color);
                //// predict 
                var output = lle.Predict(input);
                //// save image file on disk
                LLEAPI.V1.Image.Imwrite(
                    "C://github//LLE//LLE//x64//Debug//result1.jpg",
                    output);
                //// cleanup internal instance
                lle.Shutdown();
            }
            catch (Exception ex)
            {
                System.Diagnostics.Debug.WriteLine(ex.ToString());
            }
        }
    }
}

Roadmap

  • [x] Provide a managed NuGet wrapper for .NET / C# (LLE.Managed.Cuda118)
  • [ ] Improve .NET API ergonomics (more idiomatic C# surface)
  • [ ] Add additional runtime variants (e.g., different CUDA versions)
  • [ ] Improve low-light enhancement model quality and provide more model options/variants

Research References / Acknowledgements

This project uses ideas and/or model architectures from academic research. If you use LLE in research, demos, or publications, please consider citing the original papers.

We sincerely thank the authors and contributors of these works for advancing low-light enhancement research:

  • Zero-DCE (CVPR 2020)
    Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, Runmin Cong
    Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
    Paper (CVF Open Access): https://opena

gellston

View profile

View on GitHub
GitHub Stars7
CategoryEducation
Updated2mo ago
Forks0
gellston/LLE

Languages

C

Security Score

90/100

Audited on Jan 10, 2026

No findings