# HIEN: A Hybrid Interaction Enhanced Network for Horse Iris Super-Resolution
## 2 Materials and Methods  
### Horse Iris Dataset  
The horse iris dataset used in this study was obtained from Zhaosu Horse Farm in Zhaosu County, Ili Kazakh Autonomous Prefecture, Xinjiang Uygur Autonomous Region, China, in July 2024 (Latitude: 43\({}^{\circ}\)07\({}^{\prime}\)05\({}^{\prime\prime}\) N, Longitude: 81\({}^{\circ}\)00\({}^{\prime}\)02\({}^{\prime\prime}\) E). The study cohort comprised 54 horses, from which iris data were collected from both eyes, resulting in a total of 108 distinct eye samples. Data acquisition was conducted using a Raspberry Pi 4B to capture images with a Near-Infrared (NIR) camera operating in video mode from a distance of approximately 4-6 cm from the horse's eye. For each eye, video sequences of approximately 30 s were recorded at a resolution of 640 \(\times\) 480 pixels and a frame rate of 30 frames per second (fps). From each resulting video, 10 image frames were subsequently extracted for analysis. To mitigate redundancy and ensure sample diversity, frames were selected with sufficient temporal separation. A schematic diagram of the horse's eye structure is presented in Figure 1, and the data-collection environment and process are illustrated in Figure 2 [11]. Following the extraction stage, the images were processed. Utilizing the LabelMe annotation tool, the iris region in each image was manually segmented with precision, resulting in 256 \(\times\) 256 pixel iris patches. These patches served as the ground-truth HR samples for this research. Simultaneously, corresponding iris masks were created to aid the subsequent iris-recognition procedures. To augment the training dataset and improve the model's robustness to rotational variance, data augmentation involving rotations of \(\pm\)15 degrees was applied to each HR iris patch. Given the uniqueness of the iris, we treated the 108 eyes from 54 horses as 108 independent subjects for our dataset partitioning. The samples for each subject were organized into folders labeled '000' to '107'. A deterministic allocation strategy was then applied based on the last digit of the folder identifier: samples in folders with identifiers ending in 0, 5, or 9 were assigned to the combined validation and test sets, while the remaining folders were used for training. A detailed breakdown of the dataset distribution is presented in Table 1, and representative examples of the acquired iris samples are depicted in Figure 3.  
Figure 1: Surface anatomy of the horse eye and adnexa revealed by near-infrared illumination: (A) infrared light source reflection, (B) pupil, (C) granulae iridica, (D) iris, (E) sclera. Low-resolution (LR) iris images were synthesized from their HR counterparts via bicubic downsampling, followed by the application of Gaussian blurring. This process was designed to simulate the image degradation typically encountered under practical acquisition conditions. An \(\times\)4 scaling factor was selected for the primary SR task in this \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Training Set** & **Test Set** & **Validation Set** \\ \hline Number of Horses & 76 & 16 & 16 \\ Number of Images & 2280 & 480 & 480 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset distribution. Figure 3: Example of horse iris images. The first row shows high-resolution images, and the second row shows the corresponding low-resolution images. Figure 2: (**a**) Data-collection environment. (**b**) Data-collection device (left: near-infrared camera; right: Raspberry Pi 4B). (**c**) Data collection process. study, based on a considered trade-off between the practicalities of image acquisition and the achievable quality of image reconstruction. From a practical standpoint, an x4 scaling factor permits image acquisition from a safer distance, minimizing disturbance and stress to the animals, thereby enhancing the overall efficiency and feasibility of the data-collection process. Conversely, regarding reconstruction quality, the \(\times\)4 factor represents a favorable balance compared to higher factors (e.g., \(\times\)8).