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Related Experiment Video

Updated: May 5, 2026

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Visual-in-Visual: A Unified and Efficient Baseline for Image Restoration.

Yuning Cui, Wenqi Ren, Boxin Shi

IEEE Transactions on Pattern Analysis and Machine Intelligence

|March 3, 2026

Summary

This summary is machine-generated.

VIVNet is a novel image restoration model inspired by the human visual system. It achieves high accuracy and efficiency across diverse tasks, offering a practical solution for complex image restoration challenges.

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Area of Science:

Computer Vision
Artificial Intelligence
Biologically Inspired Computing

Background:

Image restoration research faces a trade-off between performance and computational efficiency.
Existing methods often have limited applicability across various degradation types and datasets.

Purpose of the Study:

To introduce VIVNet, a unified baseline model for image restoration that balances accuracy and efficiency.
To demonstrate the model's versatility across a wide spectrum of image restoration tasks and datasets.

Main Methods:

VIVNet integrates a biologically inspired micro visual module within a U-shaped architecture.
The module employs lightweight depth-wise convolutions, similarity-aware weighting, and iterative element-wise multiplication.
This design mimics human visual processing for enhanced feature extraction and dependency capture.

Main Results:

VIVNet demonstrates competitive performance in image restoration tasks.
The model achieves high computational efficiency, making it practical for real-world applications.
Evaluations across general, all-in-one, composite degradation, UHD, underwater, medical, and remote sensing datasets confirm its robustness.

Conclusions:

VIVNet offers a strong and efficient solution for image restoration, inspired by the human visual system.
Its unified architecture and biologically inspired design enable high performance across diverse and challenging scenarios.
The model presents a practical advancement in the field of image restoration.