Hybrid Convolutional Transformer with Dynamic Prompting for Adaptive Image Restoration

Jinmei Zhang, Guorong Chen (), Junliang Yang, Qingru Zhang, Shaofeng Liu and Weijie Zhang
Additional contact information
Jinmei Zhang: Department of School of Computer Science and Engineering (School of Artificial Intelligence), Chongqing University of Science and Technology, Chongqing 401331, China
Guorong Chen: Department of School of Computer Science and Engineering (School of Artificial Intelligence), Chongqing University of Science and Technology, Chongqing 401331, China
Junliang Yang: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China
Qingru Zhang: Department of School of Computer Science and Engineering (School of Artificial Intelligence), Chongqing University of Science and Technology, Chongqing 401331, China
Shaofeng Liu: Department of School of Computer Science and Engineering (School of Artificial Intelligence), Chongqing University of Science and Technology, Chongqing 401331, China
Weijie Zhang: Department of School of Computer Science and Engineering (School of Artificial Intelligence), Chongqing University of Science and Technology, Chongqing 401331, China

Mathematics, 2025, vol. 13, issue 20, 1-17

Abstract: High-quality image restoration (IR) is a fundamental task in computer vision, aiming to recover a clear image from its degraded version. Prevailing methods typically employ a static inference pipeline, neglecting the spatial variability of image content and degradation, which makes it difficult for them to adaptively handle complex and diverse restoration scenarios. To address this issue, we propose a novel adaptive image restoration framework named Hybrid Convolutional Transformer with Dynamic Prompting (HCTDP). Our approach introduces two key architectural innovations: a Spatially Aware Dynamic Prompt Head Attention (SADPHA) module, which performs fine-grained local restoration by generating spatially variant prompts through real-time analysis of image content and a Gated Skip-Connection (GSC) module that refines multi-scale feature flow using efficient channel attention. To guide the network in generating more visually plausible results, the framework is optimized with a hybrid objective function that combines a pixel-wise L1 loss and a feature-level perceptual loss. Extensive experiments on multiple public benchmarks, including image deraining, dehazing, and denoising, demonstrate that our proposed HCTDP exhibits superior performance in both quantitative and qualitative evaluations, validating the effectiveness of the adaptive restoration framework while utilizing fewer parameters than key competitors.

Keywords: image restoration; dynamic prompting; transformer; adaptive restoration; attention mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/20/3329/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/20/3329/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:20:p:3329-:d:1774762

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().