---
library_name: pytorch
tags:
- image-denoising
- computer-vision
- u-net
- image-restoration
- cbam
- image-processing
metrics:
- psnr
- mae
- mse
datasets:
- BSDS500
pipeline_tag: image-to-image
---

# LAR-U-Net: Lightweight Attentive Residual U-Net for Image Denoising


> **Course Project Notice:** This repository and model card were developed as part of the academic project for **`ARTI404: Image Processing`**.

**LAR-U-Net** is an advanced, lightweight image restoration architecture engineered specifically for grayscale image denoising. It structurally combines the spatial localization precision of a symmetric **U-Net** backbone, adaptive feature refinement via the **Convolutional Block Attention Module (CBAM)**, and a **Residual Noise Learning** paradigm. Additionally, it leverages a handcrafted **Gradient-Augmented Processing** front-end to strictly preserve structural boundaries and high-frequency edge dynamics during restoration.

---

## Architecture Overview

The primary challenge in discriminative image denoising is avoiding the over-smoothing of critical high-frequency details (e.g., fine textures, sharp edges). LAR-U-Net addresses this through a four-tiered architectural design:

1. **Gradient-Augmented Processing (Front-End):** Instead of processing the raw noisy image in isolation, the input is passed through horizontal (G_x) and vertical (G_y) Sobel operators. The extracted gradient magnitude map (G) is concatenated along the channel dimension, forcing early spatial layers to prioritize structural edge maps.
2. **Symmetric U-Net Backbone:** Utilizes an encoder-decoder architecture with skip connections, balancing parameter count and feature representation using optimized 3X3 convolutions.
3. **Dual-Axis Attention Gates (CBAM):** Embedded directly inside each encoder stage to dynamically re-weight features:
   * **Channel Attention:** Compresses spatial dimensions using adaptive global average pooling to capture cross-channel interdependencies (distinguishing signal from noise variants).
   * **Spatial Attention:** Concatenates average and max pooling feature maps across the channel dimension and applies a 7X7 kernel to generate an explicit spatial mask highlighting destructive noise regions.
4. **Residual Noise Learning Paradigm:** Adopting the discriminative mapping strategy popularized by **DnCNN**, the network directly predicts the underlying *Noise Map* R(y) rather than the clean image itself.

---

## Mathematical Formulation

### 1. Image Degradation & Reconstruction Model
Given an observed noisy image y degraded by additive noise:

$$y = x + v$$

Where x denotes the latent clean image and v represents the corrupting Additive White Gaussian Noise (AWGN) component with standard deviation. The restored image (x) is computed via explicit residual subtraction:

$$\hat{x} = y - R(y)$$

### 2. Front-End Edge Gradient Extraction
Spatial edge dynamics are computed using discrete Sobel spatial operators:

$$G = \sqrt{G_x^2 + G_y^2}$$

### 3. Optimization Criterion
The network parameters (W) are optimized by minimizing the Mean Squared Error (MSE) loss regularized explicitly over the target residual noise map:

$$\mathcal{L}(W) = \frac{1}{2N} \sum_{i=1}^{N} \| R(y_i; W) - (y_i - x_i) \|_F^2$$

---

## Performance & Experimental Results

### Training Hyperparameters
* **Optimizer:** Adam
* **Loss Function:** Mean Squared Error (MSE)
* **Epochs:** 10
* **Batch Size:** 8
* **Target Noise Setup:** AWGN (sigma = 25)
* **Core Dataset:** Berkeley Segmentation Dataset 500 (**BSDS500**) — using disjoint train/val/test splits.
* **Robustness Evaluation Suite:** Multi Noises Dataset (Evaluated against Speckle, Poisson, Multiplicative, JPEG artifacts, Quantization, and Salt & Pepper noise).

### Quantitative Metrics
* **Minimum Training Loss:** `0.00315` (MSE)
* **Minimum Validation Loss:** `0.00327` (MSE)
* **Test Dataset MAE:** `0.04253`
* **Average Suite PSNR:** `19.9205 dB`
* **Peak PSNR Gain:** **> +7.0 dB** on structured natural scenes.

#### Training Dynamics
| Epoch | Train Loss (MSE) | Validation Loss (MSE) |
| :---: | :--------------: | :------------------: |
| 1     | 0.22759          | 0.24682              |
| 2     | 0.00983          | 0.00924              |
| 5     | 0.00415          | 0.00414              |
| **10**| **0.00315**      | **0.00327**          |

#### Sample-Level Reconstruction Analysis
| Evaluation Parameter | Sample 1 | Sample 2 |
| :------------------- | :------: | :------: |
| **Initial Noisy PSNR** | 20.21 dB | 20.19 dB |
| **Denoised PSNR** | 28.01 dB | 27.36 dB |
| **Net PSNR Gain** | **+7.79 dB** | **+7.18 dB** |
| **Initial MAE** | 0.0779   | 0.0783   |
| **Denoised MAE** | 0.0306   | 0.0331   |
| **Net MAE Reduction**| **-0.0473** | **-0.0452** |

> **Architectural Note on Domain Generalization:** While LAR-U-Net yields quantitative improvements on impulsive noise types (e.g., Salt & Pepper), visual quality inspection confirms its performance is optimal for **Gaussian-distributed (AWGN) noise**. This is due to the inherent symmetry of residual noise mappings and Batch Normalization layers operating over normal probability distributions during SGD optimization.

---

## Comparison with Alternative Architectures & Trade-offs

| Model Architecture | Key Strengths | Key Weaknesses | Optimization Loss |
| :--- | :--- | :--- | :--- |
| **Standard U-Net** | High spatial precision via skip connections; robust multi-scale localization. | High memory footprint; prone to producing blurry edges when trained on MSE alone. | MSE Loss / L1 Loss |
| **DnCNN** | Excellent Gaussian noise mapping via residual learning and Batch Normalization. | Lacks spatial attention; sensitive to structural domain shifts; computationally heavier at single resolution. | Residual MSE Loss |
| **Restormer** | State-of-the-art global contextual modeling via Transposed Self-Attention; exceptional benchmark results. | Extremely computationally expensive; heavy GPU memory overhead; requires huge datasets. | Frequency Loss |
| **LAR-U-Net** *(Ours)* | **Lightweight**; explicit edge preservation via Sobel front-end; spatial/channel feature refinement via **CBAM**. | Performance drops on heavy non-Gaussian noise distributions (e.g., extreme Salt & Pepper). | MSE Loss |