App. Res. of Comp. Accepted Paper
Vol. 44, 2027 No. 1

Spu loss:structure-preserving uncertainty loss for single image super-resolution

Zhang Lei1,2,3
Liu Yupeng1,2,3
Lian Shuaishuai4
Wang Shuai1,2,3
Wu Mingxi1,2,3
Zhang Yunxiang1,2,3
1. School of Information and Electronic Technology, Jiamusi University, Jiamusi Heilongjiang 154007, China
2. Heilongjiang Province Key Laboratory of Autonomous Intelligence and Information Processing, School of Information and Electronic Technology, Jiamusi University, Jiamusi Heilongjiang 154007, China
3. Jiamusi Key Laboratory of Satellite Navigation Technology and Equipment Engineering Technology, School of Information and Electronic Technology, Jiamusi University, Jiamusi Heilongjiang 154007, China
4. Handan Vocational College of Science and Technology, Handan 056046, Hebei, China
DOI: 10.19734/j.issn.1001-3695.2026.04.0127

Abstract

To mitigate texture over-smoothing and structural distortion caused by pixel-level loss functions in image super-resolution, this study developed a Structure-Preserving Uncertainty Loss (SPU Loss) . SPU Loss utilizes local variance to characterize structural complexity. An uncertainty weighting mechanism increases the gradient weights of texture and edge regions during backpropagation. Meanwhile, a low-frequency structural consistency constraint maintains global structures. Experiments on four standard datasets across three magnification factors (×2、×3 and ×4) evaluated the performance. Quantitative indicators and visual quality demonstrate significant improvements over several representative loss functions. This study provides an effective loss function optimization scheme to resolve texture blurring and structural deformation in super-resolution reconstruction.

Foundation Support

黑龙江省本科基本科研业务费项目(2023-KYYWF-0583)
黑龙江省省属本科高校优秀青年教师基础研究支持计划(YQJH2024239)
黑龙江省自然科学基金联合基金培育项目(PL2024F002)
国家外国专家重点支撑项目(D20250185)
黑龙江省基本科研业务费基础研究项目(2019-KYYWF-1386)
佳木斯市科技计划创新激励类项目(GY2025JL0004)佳木斯大学博士专项科研启动项目(JMSUBZ2024-07)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.04.0127
Publish at: Application Research of Computers Accepted Paper, Vol. 44, 2027 No. 1

Publish History

[2026-08-27] Accepted Paper

Cite This Article

张磊, 刘宇鹏, 连帅帅, 等. 基于结构保持不确定性建模的图像超分辨率损失函数 [J]. 计算机应用研究, 2027, 44 (1). (2026-09-14). https://doi.org/10.19734/j.issn.1001-3695.2026.04.0127. (Zhang Lei, Liu Yupeng, Lian Shuaishuai, et al. Spu loss:structure-preserving uncertainty loss for single image super-resolution [J]. Application Research of Computers, 2027, 44 (1). (2026-09-14). https://doi.org/10.19734/j.issn.1001-3695.2026.04.0127. )

About the Journal

  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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