Lib.rs

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# dehazing

An image dehazing toolkit that utilizes Deep Convolutional Neural Networks (DNN) for haze removal.

## Features

- Deep learning-based efficient dehazing algorithm
- GPU acceleration support (CUDA)
- Simple and easy-to-use API interface
- Integration with [image](https://crates.io/crates/image) library for image processing

## Quick Start

Install `dehazing` and `image` using `cargo`:

```bash
cargo add dehazing image

Example Code

Below is a complete example demonstrating how to use dehazing for image dehazing:

let device = candle_core::Device::cuda_if_available(0).unwrap();
let base_dir = env!("CARGO_MANIFEST_DIR");

// Load pre-trained model
let model = DehazeNet::with_device(&device).unwrap();

// Open input image
let img = image::open(format!("{base_dir}/testdata/test2.png")).unwrap();

// Convert image to RGB8 format and transform to Tensor
let raw = img.to_rgb8().into_vec();
let data = Tensor::from_vec(
    raw,
    (img.height() as usize, img.width() as usize, 3),
    &device,
)
.unwrap()
.to_dtype(candle_core::DType::F32)
.unwrap()
.broadcast_div(&Tensor::new(255f32, &device).unwrap())
.unwrap()
.permute((2, 0, 1))
.unwrap()
.unsqueeze(0)
.unwrap();

println!("{data:?}");

// Perform dehazing inference
let out = model.forward(&data).unwrap();

// Process output tensor
let out = out.squeeze(0).unwrap(); // Remove batch dimension [c, h, w]
let (_, height, width) = out.dims3().unwrap();

// Convert output tensor to image data
let image_data: Vec<u8> = out
    .permute((1, 2, 0))
    .unwrap() // [H, W, C] matches image layout
    .flatten_all()
    .unwrap()
    .to_vec1::<f32>()
    .unwrap()
    .iter()
    .map(|&v| (v.clamp(0.0, 1.0) * 255.0) as u8)
    .collect();

// Save image
let img_out =
    image::RgbImage::from_raw(width as u32, height as u32, image_data).expect("Failed to create image");

img_out.save("result/dehazed_output.jpg").expect("Failed to save image");
println!("Dehazed result saved as result/dehazed_output.jpg");

Model Description

This project implements an end-to-end dehazing model based on the DehazeNet architecture. The model uses deep convolutional neural networks to predict atmospheric light and transmission maps for image restoration.

Device Support

• CPU: Supported by default
• GPU (CUDA): Enabled via features = ['cuda'] with CUDA support

Ensure your system has proper CUDA drivers installed and enable cuda feature during compilation.

Contribution

PRs and Issues are welcome! Please follow the project's coding style and documentation standards.

License

MIT Licensed. See LICENSE for details.