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The unsupervised anomaly detection problem holds great importance but remains challenging to address due to the myriad of data possibilities in our daily lives. Currently, distinct models are trained for different scenarios. In this work, we introduce a reconstruction-based anomaly detection structure built on the Latent Space Denoising Diffusion Probabilistic Model (LDM). This structure effectively detects anomalies in multi-class situations. When normal data comprises multiple object categories, existing reconstruction models often learn identical patterns. This leads to the successful reconstruction of both normal and anomalous data based on these patterns, resulting in the inability to distinguish anomalous data. To address this limitation, we implemented the LDM model. Its process of adding noise effectively disrupts identical patterns. Additionally, this advanced image generation model can generate images that deviate from the input. We have further proposed a classification model that compares the input with the reconstruction results, tapping into the generative power of the LDM model. Our structure has been tested on the MNIST and CIFAR-10 datasets, where it surpassed the performance of state-of-the-art reconstruction-based anomaly detection models. 

The rapid accessibility of portable and affordable retinal imaging devices has made early differential diagnosis easier. For example, color funduscopy imaging is readily available in remote villages, which can help to identify diseases like age-related macular degeneration (AMD), glaucoma, or pathological myopia (PM). On the other hand, astronauts at the International Space Station utilize this camera for identifying spaceflight-associated neuro-ocular syndrome (SANS). However, due to the unavailability of experts in these locations, the data has to be transferred to an urban healthcare facility (AMD and glaucoma) or a terrestrial station (e.g., SANS) for more precise disease identification. Moreover, due to low bandwidth limits, the imaging data has to be compressed for transfer between these two places. Different super-resolution algorithms have been proposed throughout the years to address this. Furthermore, with the advent of deep learning, the field has advanced so much that 2 and 4 compressed images can be decompressed to their original form without losing spatial information. In this paper, we introduce a novel model called Swin-FSR that utilizes Swin Transformer with spatial and depth-wise attention for fundus image super-resolution. Our architecture achieves Peak signal-to-noise-ratio (PSNR) of 47.89, 49.00 and 45.32 on three public datasets, namely iChallenge-AMD, iChallenge-PM, and G1020. Additionally, we tested the model’s effectiveness on a privately held dataset for SANS and achieved comparable results against previous architectures.