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Updated: Jun 22, 2025

Diffusion Imaging in the Rat Cervical Spinal Cord

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Universal Image Restoration with Text Prompt Diffusion.

Bing Yu¹, Zhenghui Fan¹, Xue Xiang¹

¹Shanghai Film Academy, Shanghai University, Shanghai 200072, China.

Sensors (Basel, Switzerland)

|June 27, 2024

Summary

This summary is machine-generated.

This study introduces ETDiffIR, a novel diffusion model (DM) framework for universal image restoration (UIR). It uses text prompts to guide image restoration, improving performance across diverse degradations like dehazing, deraining, and denoising.

Keywords:

diffusion model image restoration text prompt

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Universal Image Restoration (UIR) aims to recover images from various unknown degradations.
Existing UIR methods struggle with extracting degradation information and lack universality.
Current approaches often yield suboptimal recovery outcomes due to inaccurate estimations.

Purpose of the Study:

To propose an effective framework for Universal Image Restoration (UIR) using a diffusion model (DM).
To enhance UIR performance by incorporating text prompts, inspired by their success in image generation.
To address limitations of existing methods, including poor enhancement and low universality.

Main Methods:

Developed ETDiffIR, a framework based on a diffusion model (DM) for UIR.
Employed text prompts to guide the diffusion model in restoring degraded images.
Introduced a novel text-image fusion block combining CLIP text encoder and DA-CLIP image controller.
Integrated text prompt and degradation type encoding into time step encoding.
Designed an efficient restoration U-shaped network (ERUNet) using depthwise and pointwise convolutions to reduce computational cost.

Main Results:

The proposed ETDiffIR framework demonstrates superior performance on image dehazing, deraining, and denoising tasks.
Experimental results validate the effectiveness of using text prompts for UIR.
The integration of text-image fusion and ERUNet contributes to improved restoration quality and efficiency.

Conclusions:

ETDiffIR offers a promising approach for Universal Image Restoration (UIR) by leveraging text prompts.
The method shows significant improvements over existing techniques in handling diverse image degradations.
The framework provides a more universal and effective solution for image restoration challenges.