varun0908/Stripformer_TTA_Deblurring

Stripformer TTA Ensembling (Image Deblurring)

This repository provides a PyTorch implementation of an image deblurring model based on the official Stripformer architecture (ECCV 2022), trained on the GoPro Motion Deblurring Dataset.

The released model includes a full Test-Time Augmentation (TTA) ensembling pipeline and post-processing enhancements, improving robustness on real-world motion blur.

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📘 Conceptual Walkthrough (Medium Article)

This model is accompanied by a detailed conceptual explanation of modern image deblurring, written to explain how and why neural networks learn to undo blur in a clear, step-by-step, and intuitive manner.

The article walks through the full deblurring pipeline—from feature extraction and spatial reasoning to coarse-to-fine refinement and artifact suppression—and uses Stripformer + TTA as a concrete reference throughout to bridge theory with practice.

👉 Read the full explanation here:
https://medium.com/@varunpatels2004/how-neural-network-learns-to-undo-the-blur-4751bbf86f29

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Architecture and References

• Model Architecture: Stripformer (ECCV 2022, official design)
• Paper: https://arxiv.org/abs/2204.04627
• Official GitHub Repository:
  https://github.com/pp00704831/Stripformer-ECCV-2022-

The architecture follows an encoder–transformer–decoder design with strip-based self-attention and residual learning (output + input).

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Model Overview

• Task: Image Deblurring (Image-to-Image)
• Framework: PyTorch
• Training Data: GoPro Motion Deblurring Dataset
• Inference: Single-image inference with full TTA
• Checkpoint format: PyTorch state_dict
• License: Apache-2.0

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Training Details

• Dataset: GoPro Motion Deblurring Dataset
• Supervision: Paired blurry–sharp images
• Blur Type: Realistic motion blur
• Image Domain: Natural RGB images

The model was trained using the official Stripformer architecture without architectural modification.

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Inference Pipeline

The provided inference.py reproduces the exact inference pipeline used during evaluation.

Test-Time Augmentation (TTA)

The following augmentations are applied:

• Identity
• Horizontal flip
• Vertical flip
• Horizontal + vertical flip
• Transpose

Each prediction is de-augmented and averaged to produce the final output.

Post-Processing

• High-frequency detail enhancement
• Adaptive CLAHE (contrast normalization)

These steps improve perceptual sharpness and stability for strong motion blur.

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Evaluation Summary (Limited)

A focused evaluation was conducted on 10 extreme-blur samples.

Metric	Observation
Best base PSNR	~31.71 dB
TTA PSNR gain	+0.14 to +0.33 dB
Avg SSIM improvement	+0.0039
Consistency	Improvement on all tested samples

These results illustrate consistent but modest gains from TTA.

This is not a full benchmark evaluation.

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Qualitative Results

The following examples compare:

• Blurred input
• Base Stripformer output
• Stripformer + full TTA
• Ground-truth sharp image

Example 1

Example 2

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Repository Structure

File	Description
best_model.pth	Trained model weights (state_dict)
inference.py	Full inference pipeline with TTA
requirements.txt	Python dependencies
config.json	Model metadata
.gitattributes	Git LFS configuration

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Installation

pip install -r requirements.txt