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A Cosine Network for Image Super-Resolution

arXiv 2601.16413 ↗First posted 23 Jan 2026cs.CVcited by 0

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Abstract (arXiv)

Deep convolutional neural networks can use hierarchical information to progressively extract structural information to recover high-quality images. However, preserving the effectiveness of the obtained structural information is important in image super-resolution. In this paper, we propose a cosine network for image super-resolution (CSRNet) by improving a network architecture and optimizing the training strategy. To extract complementary homologous structural information, odd and even heterogeneous blocks are designed to enlarge the architectural differences and improve the performance of image super-resolution. Combining linear and non-linear structural information can overcome the drawback of homologous information and enhance the robustness of the obtained structural information in image super-resolution. Taking into account the local minimum of gradient descent, a cosine annealing mechanism is used to optimize the training procedure by performing warm restarts and adjusting the learning rate. Experimental results illustrate that the proposed CSRNet is competitive with state-of-the-art methods in image super-resolution.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 33 of its 78 references that are in the map. First posted on arXiv 23 Jan 2026.

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33 of 78 references.

References · 33

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21 more
  1. 2015Deeply-Recursive Convolutional Network for Image Super-Resolutioncited by 317
  2. 2018Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Networkcited by 263
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  10. 2023Swift Parameter-free Attention Network for Efficient Super-Resolutioncited by 24
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  15. 2021Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolutioncited by 10
  16. 2025Efficient Attention-Sharing Information Distillation Transformer for Lightweight Single Image Super-Resolutioncited by 9
  17. 2024Heterogeneous window transformer for image denoisingcited by 5
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  19. 2022Stereo Superpixel Segmentation Via Decoupled Dynamic Spatial-Embedding Fusion Networkcited by 2
  20. 2024Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolutioncited by 2
  21. 2024Adaptive Convolutional Neural Network for Image Super-resolutioncited by 1

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