{"id":9239,"date":"2026-07-18T16:05:28","date_gmt":"2026-07-18T08:05:28","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9239"},"modified":"2026-07-18T17:27:55","modified_gmt":"2026-07-18T09:27:55","slug":"jase-202610-33-043","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-043","title":{"rendered":"Lightweight Adaptive Transformer for Real-Time and High- Quality Image Restoration"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Dongfang Wang<\/i><\/a><\/p>\n\n\n\n

School of Information Engineering, Zhengzhou University of Science and Technology, Zhengzhou 450064, China<\/p>\n<\/div>\n\n\n\n

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Received: March 18, 2026
Accepted: May 3, 2026
Publication Date: July 18, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Overall architecture of the proposed Lightweight Adaptive Transformer (LAT) <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.043<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Image restoration is a fundamental task in computer vision, aiming to recover high quality images from degraded inputs. While Transformer-based methods have achieved state-of-the-art (SOTA) performance in this field, their heavy computational complexity and high memory consumption severely limit the deployment in real-time scenarios. To address the trade-off between restoration quality and inference efficiency, this paper proposes a lightweight adaptive Transformer (LAT) for real-time and high-quality image restoration. Specifically, we design a novel adaptive depthwise separable attention (ADSA) mechanism, which replaces the full self attention in traditional Transformers with depthwise separable convolutions and adaptive gating, reducing computational complexity fromO (N2<\/sup>) to O(N) (where N is the number of image tokens). Additionally, a dynamic feature fusion (DFF) module is introduced to adaptively integrate multi-scale features, enhancing the restoration of fine-grained details while maintaining model lightness. Furthermore, we propose a hybrid loss function (HLF) that combines perceptual loss, L1 loss, and adversarial loss, balancing objective accuracy and subjective visual quality. Extensive experiments are conducted on five benchmark datasets (DIV2K, Set14, BSD100, Urban100, and RealSR) for super-resolution, denoising, and deblurring tasks. Results demonstrate that the proposed LAT achieves SOTA restoration quality (e.g., PSNR of 38.21 dB and SSIM of 0.961 on DIV2K for 4\u00d7 super-resolution) while reducing the model parameters by 72.3% and inference time by 68.5% compared to existing Transformer-based methods. Ablation studies verify the effectiveness of each proposed module, and real-world tests confirm its applicability in real-time scenarios.<\/p>\n\n\n\n

Keywords: Image Restoration; Lightweight Transformer; Adaptive Attention; Real-Time Inference; Feature Fusion<\/em><\/p>\n\n\n\n

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