Research on Super-Resolution Enhancement Technology Using Improved Transformer Network and 3D Reconstruction of Wheat Grains

• Published in: IEEE access Vol. 12; p. 1
• Main Authors: Tian, Yijun, Zhang, Jinning, Zhang, Zhongjie, Wu, Jianjun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Botany; Channel attention; Crops; Datasets; Flowers & plants; Grain; Harnesses; Image enhancement; Image reconstruction; Image resolution; Moisture; Neural networks; Phenotypes; Signal to noise ratio; Solid modeling; Super-resolution reconstruction; Superresolution; Three-dimensional displays; Three-dimensional reconstruction; Transformer; Transformers; Wheat; Wheat grains
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3396148

Three-dimensional reconstruction plays a crucial role in capturing plant phenotypes and expediting the process of agricultural informatization. However, the reconstruction of small objects such as plant specimens and grains often faces challenges like low two-dimensional image resolution and sparse textures. To enhance the three-dimensional reconstruction of plant specimens like wheat grains for comprehensive phenotypic characterization, this study proposes a novel super-resolution reconstruction network called T-transformer net. The network leverages the self-attention mechanism of Transformers to extract extensive global information from spatial sequences. By employing a hourglass block structure to construct spatial attention units and combining channel attention with window-based self-attention schemes, it effectively harnesses their complementary advantages. This encompasses utilizing global statistical data while capitalizing on potent local fitting capabilities. Evaluation of the model on publicly available datasets Set5, Set14, and Manga109 demonstrates superior overall performance of T-transformer net compared to mainstream super-resolution algorithms at upscaling factors of 2x, 3x, and 4x. In the context of super-resolution tasks involving wheat grain datasets, the peak signal-to-noise ratio reaches 42.89 dB, and the structural similarity index attains 0.9643. Subsequently, we subject the super-resolved wheat grain images to three-dimensional reconstruction. Through comprehensive extraction of high-level semantic information by neural networks, the reconstruction accuracy is improved by 38.96% compared with the unprocessed image, effectively mitigating challenges arising from sparse textures and repetitive patterns in wheat grain structures. This study contributes valuable methodology and insights to the realm of three-dimensional reconstruction in botany, holding significant implications for advancing agricultural informatization.