# HIEN: A Hybrid Interaction Enhanced Network for Horse Iris Super-Resolution
## 1 Introduction  
Horsese valued as both economic livestock and companion animals play indispensable roles in agriculture, sport, recreation, and scientific research [1, 2, 3]. As the horse industry grows and demands more refined management, an accurate, efficient, and reliable system for individual horse identification becomes essential [4]. Precise identification underpins effective breeding management, pedigree tracking, health monitoring, and ownership verification, and it is also crucial for ensuring fair competition, deterring theft and fraud, and implementing disease-control and quarantine measures [5, 6, 7]. Hence, developing advanced horse-identification technologies offers significant advantages [8]. Traditional identification methods such as branding and descriptions based on coat color, hair whorls, or white markings have various limitations [9]. Among biometric traits,the iris is widely recognized as one of the most promising markers due to its unique, highly complex texture and lifelong stability [10]. Horse irises exhibit rich, individually distinctive patterns that mature shortly after birth and remain stable over time [11]. Compared with conventional approaches, iris-based recognition is contact-free, highly resistant to forgery, and welfare-friendly, offering a more precise and secure means of identifying horses. Despite its promise, capturing high-quality horse iris images poses formidable challenges. As large, sensitive animals, horses rarely remain still during imaging; head and eye movements are pervasive [12]. For safety and convenience, cameras must typically be positioned at some distance, which further degrades resolution and sharpness. The resulting low-resolution, blurred images obscure critical texture details, undermining feature-extraction and -matching accuracy and thus limiting overall system performance. Recent advances in deep learning and natural image super-resolution reconstruction have opened the door to iris image enhancement. In 2014, [PERSON] et al. [13] introduced SRCNN (Super-Resolution Convolutional Neural Network), the first CNN-based framework for image super-resolution. The SRCNN pipeline begins by upsampling the low-resolution input image via bicubic interpolation to the target high-resolution size, followed by refinement through a three-layer convolutional network for image reconstruction and detail enhancement. In the human iris super-resolution domain, many studies have demonstrated that enhancing resolution can boost recognition accuracy. [PERSON] et al. [14] employed transfer learning to show that CNN-learned features improve iris-recognition rates, confirming their efficacy. [PERSON] et al. [15] proposed an SRGAN-based approach, using a generator-discriminator adversarial process to produce high-quality iris images and a deep CNN for identity evaluation. [PERSON] et al. [16] developed a deep learning framework integrating wavelet decomposition and attention mechanisms: high-frequency components are predicted via wavelet subbands, and residual attention and convolutional transformer modules extract texture details. More recently, [PERSON] et al. [17] combined Swin Transformer residual blocks with a GAN: progressive sub-pixel upsampling reconstructs high-definition irises, while a VGG-style relative discriminator preserves high-frequency texture. High-resolution (HR) restoration of iris images, which are characterized by intricate and densely textured patterns, remains challenging because most methods cannot simultaneously maintain fine local details and global consistency. CNN-based approaches, which rely on fixed receptive fields, are inherently limited in modeling dependencies over long spatial ranges [18]. In contrast, Transformer-based models leverage self attention to model global feature interactions, yet they inherently lack an inductive bias towards local structures, making them less effective in reconstructing fine-grained details [19].