Related papers: A Cosine Network for Image Super-Resolution

A Cosine Network for Image Super-Resolution

URL: http://arxiv.org/abs/2601.16413v1
Date: Fri, 23 Jan 2026 02:58:57 GMT
Title: A Cosine Network for Image Super-Resolution
Authors: Chunwei Tian, Chengyuan Zhang, Bob Zhang, Zhiwu Li, C. L. Philip Chen, David Zhang,
Abstract summary: We propose a cosine network for image super-resolution (CSRNet)<n>To extract complementary structural information, odd and even heterogeneous blocks are designed.<n>CSRNet is competitive with state-of-the-art methods in image super-resolution.
Score: 77.21621944789466
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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