README.md

metadata

title: Denoiser-Server
emoji: 🚀
colorFrom: indigo
colorTo: purple
sdk: docker
app_port: 8080
app_file: Dockerfile
pinned: false

#Image Denoising using UNET and it's variants A Deep Learning approach to remove noise from images.

##1. Project Problem Statement: The Critical Need for Image Denoising Digital images are indispensable data sources across numerous high-stakes industries, yet they are universally susceptible to noise corruption introduced during acquisition, transmission, or processing. This noise, whether Gaussian, Poisson, or Speckle, degrades images in two critical ways:

Impairing Human Perception: Noise obscures subtle features and textures, significantly lowering the visual fidelity required for accurate human interpretation.

Compromising Machine Reliability: Noise introduces spurious data points that confuse downstream Computer Vision tasks, drastically reducing the accuracy of algorithms used for analysis and automation.

The challenge is magnified across essential fields:

In Medical Imaging (e.g., MRI, CT), noise threatens the ability to identify critical, life-saving diagnostic features.

In Industrial Quality Control, noise leads to costly false positives or false negatives during automated inspection.

In Remote Sensing and Astronomy, noise prevents the reliable extraction of scientific data from satellite and telescopic imagery.

The objective of this project is to develop and evaluate a robust image denoising solution capable of effectively suppressing varied noise types while preserving crucial structural details, thereby elevating the reliability and precision of visual data for both human experts and advanced Machine Learning systems.

##2. Few Results

##3. Project Structure

├── .gitignore
├── .gitattributes
├── .github/workflows
│ ├── sync_to_hf.yml
├── Dockerfile
├── api-test.py
├── handler.py
├── requirements.txt
├── images
└── README.md

• Dataset Preparation
• UNET_training
• Residual-UNET_training
• CBAM-Residual-UNET_training
• TorchScript_comparison - Model Archiving

##4. Dataset Used

The model was trained on an augmented dataset of 32,000 clean/noisy patch pairs derived from the BSD500 dataset, utilizing a 128×128 patch size with dynamic D4 geometric augmentation. To ensure robustness against real-world degradation, we employed a hybrid noise model incorporating four components:

Mixed Sensor Noise: A combination of Additive White Gaussian Noise (σstd ∈[0,30]) and Signal-Dependent Poisson Noise (a∈[0,0.05]).

Impulse Noise: Sparse Salt-and-Pepper noise (∈[0.001,0.005]).

Structured Artifacts: JPEG compression with randomized quality (∈[70,95]).

Due to the complex, non-linear nature of this hybrid noise model, we quantified the overall degradation using the Effective Noise Level (σ eff), defined as the Standard Deviation of the entire noise residual (y−x) across the validation set. The measured effective noise level for the challenging dataset was σeff =79.32 (scaled to 0-255). All performance metrics (PSNR, SSIM) presented below are reported against this highly degraded baseline.

• Original Berkeley Segmentation Dataset 500 (BSDS500) \
• GDrive link for our modified Dataset

##5. Model Architectures

Model	Description	Key Features
U-Net	Baseline architecture for image-to-image restoration	Encoder-decoder skip connections
Residual U-Net	Adds residual blocks to improve feature flow	Residual connections within U-net blocks
Residual U-Net + CBAM	Incorporates Convolutional Block Attention Module	focuses noise removal on key locations

##6. Training Setup

Platform	Purpose	Notes
Google Colab	Dataset prep + initial testing	Limited GPU runtime
Kaggle	Model training	Used for high-performance GPUs
Google Drive	Model & dataset storage	For cross-platform access

##7. Optimization Comparing ordinary serlialization vs TorchScript inference time

Model	Speedup
U-Net	39.18 %
Residual U-Net	43.77 %
Attention Residual U-Net	30.72 %

##8. Deployment (Backend)

Backend Framework: TorchServe

Containerization: Docker

Deployment Platform: Hugging Face Spaces

HuggingFace Space link: here

Artifacts: .mar model files stored here

##8. Frontend (Next.js)

Repo: Frontend Repo Link

Platform: Vercel

Provides a simple web interface for uploading noisy images and visualizing denoised outputs.

Open Web Frontend here

##9. Results:

Model	PSNR	SSIM	Notes
U-Net	28.7583	0.8444	Baseline
Residual U-Net	28.7630	0.8415	Better texture recovery
Residual U-Net + CBAM	29.0086	0.8485	Best performance

##10. References

[1] U-Net: Convolutional Networks for Biomedical Image Segmentation
[2] Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
[3] Layer Normalization
CBAM: Convolutional Block Attention Module
[4] Attention-based UNet enabled Lightweight Image Semantic Communication System over Internet of Things
[5] Application of ResUNet-CBAM in Thin-Section Image Segmentation of Rocks

##11. Author

Rajeev Ahirwar

Linkedin
GitHub