ClearView - Tackling Dehazing Challenges

Authors : Rohit Shrothrium (rs8031), Savinay Shukla (ss16924)

Lightweight De-Hazification model with 1800 trainable params and 8MiB in size.

Our approach employs depth-wise separable convolutional layers and self-regularized activation functions to effectively clean hazy images and can be integrated with segmentation models for enhanced performance. We believe that our approach will be particularly useful for memory- bound systems already confined by heavy segmentation models when dealing with obscured environments. Our benchmark results based on SSIM and PSNR scores demonstrate that our modified architecture achieves performance on par with the DehazeNet model while having a smaller overall memory footprint. This makes our approach a promising solution for memory-bound autonomous systems that use computer vision in obscured environments, as it offers efficient and effective dehazing capabilities with reduced computational requirements.

Instruction

Dataset - https://drive.google.com/file/d/1OpokedZ9o-RP7n8Vb3CBjX2yZm-E2Qb2/view?usp=share_link

Credit - Ancuti, Cosmin, Codruta O. Ancuti, and Christophe De Vleeschouwer. ”D-hazy: A dataset to evaluate quantitatively dehazing algorithms.” 2016 IEEE international conference on image processing (ICIP). IEEE, 2016

Download the dataset and extract in the project’s directory, before running the experiment. This will create a data folder.

Note: The folder structure is as follows

CLEAR_VIEW (Project root dir)
    |
    |
    ---- data
    |        |
    |        |---haze (Hazy images set)
    |        |
    |        |
    |        |---original (Original images set)
    |
    |----(rest of your project files)

1. driver.py is the start point of execution
2. It takes in multiple args

python3 driver.py \
    --og_img=data/original/ \
    --hazy_img=data/haze/ \
    --lr=0.0004 \
    --weight_decay=0.0001 \
    --grad_clip_norm=0.1 \
    --num_epochs=10 \
    --train_batch=8 \
    --val_batch=8 \
    --num_workers=4 \
    --snapshots_folder=snapshots/ \
    --model=clearview \
    --run_id=clearview \
    --logs=logs/

3. Inorder to run "clearview" model use option --model=clearview else --model=dehaze for DehazeNet
4. Use examples.ipynb to visualize various results

Note: The training process outputs Run logs in logs folder and the model- clearview.pth in snapshots folder.

Analysis

Due to reduced complexity of our implementation (using depth-wise convolutional layers) we were able to achieve faster training times when compared to the Dehazenet architecture.

Average Training Time Comparision

Additionally, our PSNR and SSIM scores are on par with the Dehazenet scores.

PSNR Comparision	SSIM Comparision

Sample images from real-world instances.

Original Hazy images on the left, and cleaned images from our model on the right.