Genotype imputation (GI) plays a critical role in predicting missing genetic information for genomic studies and breeding applications. Although recent reference-free deep learning approaches have demonstrated promising performance, they often fail to exploit local genomic information, which limits further improvements in prediction accuracy and stability. In this study, we developed ALGI, a novel method based on a sparse convolutional denoising autoencoder, which uniquely integrates local genomic window information with group-specific feature learning. Unlike conventional convolutional or autoencoder-based approaches, ALGI first applies K-means clustering to group samples according to local genomic windows, then learns hidden genotype configurations specific to each group, capturing fine-scale local patterns and complex haplotype structures. Systematic evaluation was conducted across yeast, human, and pig MHC regions under multiple scenarios, including different window sizes, missing rates, sample sizes, and numbers of variants. Results show that ALGI demonstrates consistent improvements over conventional methods (Beagle) and state-of-the-art deep learning approaches (AE, SCDA) under the evaluated settings, with enhanced accuracy, stability, and robustness. In addition, ALGI is user-friendly and publicly available. While evaluated on highly polymorphic MHC regions, its strong performance suggests applicability to less complex regions, though broader genome-wide validation is needed. This approach provides a powerful tool for genomic selection and advancing complex trait genetics in livestock and other species.