README.md

---
language:
- en
license: mit
library_name: pytorch
pipeline_tag: image-to-image
tags:
- image-restoration
- image-denoising
- super-resolution
- image-enhancement
- image-to-image
- computer-vision
- deep-learning
- pytorch
---

# DFRNet-v1 — Baseline

## Overview

DFRNet-v1 is a lightweight convolutional neural network developed as
the baseline architecture for the DFRNet image restoration project.

The model is designed as an image-to-image learning system. It accepts
a three-channel RGB image, transforms it through a convolutional
encoder, and reconstructs the output at a fixed resolution of
**640 × 1024 pixels**.

The broader DFRNet project explores deep learning approaches for:

- Image restoration
- Image denoising
- Image enhancement
- Resolution enhancement
- Super-resolution

This release represents the initial baseline architecture and provides
a reference point for future architectural experiments.

---

## How It Works

The model follows a simple encoder-decoder pipeline:

    Input RGB Image
            |
            v
    Convolutional Encoder
            |
            v
    Feature Transformation
            |
            v
    Bilinear Upsampling
            |
            v
    Output Image
    640 × 1024

The encoder extracts and transforms visual features using convolutional
layers. The decoder then uses bilinear interpolation to reconstruct the
three-channel representation at the required output resolution.

---

## Architecture

The model contains two main components:

### Encoder

The encoder progressively transforms the channel representation:

    3 → 9 → 27 → 3

It consists of:

1. ConvBlock: 3 → 9 channels
2. ConvBlock: 9 → 27 channels
3. Conv2D: 27 → 3 channels

Each ConvBlock follows:

    Conv2D (3×3)
        |
       ReLU
        |
    Conv2D (3×3)
        |
       ReLU

All convolutional layers use 3×3 kernels with padding=1, preserving
the spatial dimensions during convolution.

### Decoder

The decoder uses bilinear interpolation:

    Output resolution: 640 × 1024

The decoder contains no additional learnable parameters.

---

## Model Statistics

| Property | Value |
|---|---|
| Architecture | ImageRegressionNet |
| Framework | PyTorch |
| Input Channels | 3 |
| Channel Progression | 3 → 9 → 27 → 3 |
| Activation | ReLU |
| Convolution Kernel | 3×3 |
| Decoder | Bilinear Upsampling |
| Output Resolution | 640 × 1024 |
| Total Parameters | 10,524 |
| Trainable Parameters | 10,524 |

---

## Repository Contents

    DFRNet-v1-Baseline/
    │
    ├── DFRNet-v1-best_model.pth
    │   Trained PyTorch model weights
    │
    ├── model.py
    │   Complete model architecture
    │
    ├── architecture.txt
    │   Detailed architecture explanation
    │
    ├── model_summary.txt
    │   Layer-by-layer model summary
    │
    ├── requirements.txt
    │   Required dependencies
    │
    └── README.md
        Model documentation

---

## Installation

Install the required dependencies:

    pip install -r requirements.txt

---

## Loading the Model

```python
import torch

from model import ImageRegressionNet


device = torch.device(
    "cuda" if torch.cuda.is_available() else "cpu"
)

model = ImageRegressionNet().to(device)

checkpoint = torch.load(
    "DFRNet-v1-best_model.pth",
    map_location=device
)

model.load_state_dict(checkpoint)

model.eval()
````

---

## Basic Inference

```python
with torch.no_grad():
    output = model(input_tensor)
```

The model expects an input tensor in the format:

```
[B, 3, H, W]
```

where:

* B is the batch size
* 3 represents RGB channels
* H is the image height
* W is the image width

The output shape is:

```
[B, 3, 640, 1024]
```

---

## Important: Preprocessing

For reliable inference, the preprocessing pipeline should match the
pipeline used during training.

Important considerations include:

* Image resizing
* RGB channel ordering
* Tensor conversion
* Pixel scaling
* Normalization

Using a significantly different preprocessing pipeline may affect
model performance.

---

## Intended Use

DFRNet-v1 is intended primarily for:

* Academic experimentation
* Deep learning research
* Image restoration experiments
* Image denoising experiments
* Image-to-image regression research
* Architecture experimentation

This version should be considered a baseline research model.

---

## Limitations

DFRNet-v1 is intentionally lightweight and designed as an initial
baseline architecture.

Limitations include:

* Limited representational capacity compared with deeper models
* Dependence on the training data distribution
* Limited generalization to unseen degradation patterns
* No residual connections
* No skip connections
* No multi-scale feature extraction
* No attention mechanisms

These limitations provide opportunities for future versions of DFRNet.

---

## Future Directions

Future versions may explore:

* Residual learning
* Skip connections
* Multi-scale feature extraction
* Attention mechanisms
* Improved encoder-decoder architectures
* Improved reconstruction losses
* Perceptual losses
* Advanced denoising strategies
* Improved super-resolution approaches

---

## Project Philosophy

DFRNet is an iterative deep learning experimentation project.

The objective is not only to develop increasingly capable image
restoration models, but also to document the technical journey behind
their development.

This includes:

* Architectural decisions
* Baseline experiments
* Model behaviour
* Failure cases
* Architectural modifications
* Experimental comparisons
* Performance improvements
* Lessons learned

Each version of DFRNet represents a stage in this ongoing development
and experimentation process.

---

## Version

**DFRNet-v1 — Baseline**

---

## Author

**Indranil Bhattacharyya**

Independent deep learning research and experimentation project focused
on image restoration, denoising, and resolution enhancement.

---

## Disclaimer

This model is provided primarily for research, educational, and
experimental purposes.

Performance may vary depending on the characteristics of the input
images, degradation patterns, preprocessing pipeline, and similarity
between inference data and the training distribution.