摘要
在真实场景中,图像往往同时遭受低分辨率、压缩失真及噪声等多种退化因素影响.现有方法通常聚焦于单一退化类型,难以应对复杂的复合退化情况.为解决真实场景中普遍存在的低分辨率与JPEG压缩伪影复合退化问题,提出一种梯度引导的联合JPEG压缩伪影去除和超分辨率重建网络.该网络以超分辨率分支为主导,融合JPEG压缩伪影去除分支与梯度引导分支的非对称特征,实现了高质量图像重建.JPEG压缩伪影去除分支专注于压缩伪影抑制,缓解了主导分支的重建负担.梯度引导分支则精准估计图像梯度,引导主导分支恢复更多细节与纹理.实验结果表明,该方法提升了低分辨率JPEG压缩图像的重建质量.
In real-world scenarios,images are often affected by multiple degradation factors simultaneously,such as low resolution,compression distortions,and noise.Existing methods typically focus on addressing a single type of degradation,making them less effective when dealing with complex compound degradations.To tackle the commonly encountered compound degradation issue of low resolution and JPEG compression artifacts in real-world scenarios,we propose a gradient-guided joint JPEG compression artifact removal and super-resolution reconstruction network.The proposed network adopts the super-resolution branch as the leading branch,which asymmetrically integrates features from the JPEG compression artifact removal and gradient-guided branches to achieve highquality image reconstruction.The JPEG compression artifact removal branch focuses on suppressing compression artifacts,thereby alleviating the reconstruction burden on the leading branch.The gradient-guided branch accurately estimates image gradients to guide the leading branch in restoring fine details and textures.Experimental results demonstrate that the proposed method improves the reconstruction quality of low-resolution JPEG-compressed images.
作者
曹坪
林树冉
张淳杰
郑晓龙
赵耀
CAO Ping;LIN Shu-Ran;ZHANG Chun-Jie;ZHENG Xiao-Long;ZHAO Yao(Institute of Information Science,School of Computer Science and Technology,Beijing Jiaotong University,Beijing 100044;Visual Intelligence+X International Cooperation Joint Laboratory of Ministry of Education,Beijing Jiaotong University,Beijing 100044;State Key Laboratory of Multimodal Artificial Intelligence Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190;State Key Laboratory for Management and Control of Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190;University of Chinese Academy of Sciences,Beijing 101408)
出处
《自动化学报》
北大核心
2025年第6期1261-1276,共16页
Acta Automatica Sinica
基金
国家自然科学基金(62476021,72225011,72434005,62072026)
多模态人工智能系统全国重点实验室开放课题基金(MAIS2024106)资助。
关键词
JPEG压缩
超分辨率
图像重建
梯度先验
JPEG compression
super-resolution
image reconstruction
gradient prior
