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Accurate classification of celestial objects is essential for advancing our understanding of the universe. MargNet is a recently developed deep learning-based classifier applied to the Sloan Digital Sky Survey (SDSS) Data Release 16 (DR16) dataset to segregate stars, quasars, and compact galaxies using photometric data. MargNet utilizes a stacked architecture, combining a Convolutional Neural Network (CNN) for image modelling and an Artificial Neural Network (ANN) for modelling photometric parameters. Notably, MargNet focuses exclusively on compact galaxies and outperforms other methods in classifying compact galaxies from stars and quasars, even at fainter magnitudes. In this study, we propose enhancing MargNet's performance by incorporating attention mechanisms and Vision Transformer (ViT)-based models for processing image data. The attention mechanism allows the model to focus on relevant features and capture intricate patterns within images, effectively distinguishing between different classes of celestial objects. Additionally, we leverage ViTs, a transformer-based deep learning architecture renowned for exceptional performance in image classification tasks. We enhance the model's understanding of complex astronomical images by utilizing ViT's ability to capture global dependencies and contextual information. Our approach uses a curated dataset comprising 240,000 compact and 150,000 faint objects. The models learn classification directly from the data, minimizing human intervention. Furthermore, we explore ViT as a hybrid architecture that uses photometric features and images together as input to predict astronomical objects. Our results demonstrate that the proposed attention mechanism augmented CNN in MargNet marginally outperforms the traditional MargNet and the proposed ViT-based MargNet models. Additionally, the ViT-based hybrid model emerges as the most lightweight and easy-to-train model with classification accuracy similar to that of the best-performing attention-enhanced MargNet. This advancement in deep learning will contribute to greater success in identifying objects in upcoming surveys like the Vera C. Rubin Large Synoptic Survey Telescope. [ABSTRACT FROM AUTHOR]

High-noise astronomical-image denoising has always been a research hotspot in deep space exploration. Compressive sensing (CS) is an advanced technology used for high-dimensional signal processing. It is useful for processing high-resolution astronomical images. To obtain high-quality astronomical images, a CS spatially adaptive total variation iterative (CSSATVI) method is proposed herein. In this method, a curvelet transform based on an adaptive curvelet soft thresholding operator is proposed to adaptively remove hidden noise information in the process of image sparse representation, and a novel CS denoising reconstruction model proposed is used to deeply mine the texture, edge and other detailed information. Moreover, a novel reconstruction strategy is proposed for preserving detailed image information in the iterative reconstruction process to obtain high-quality astronomical images. Simulation results indicated that the proposed CSSATVI method can quickly reconstruct a high-quality astronomical image and preserve a large amount of astronomical image details; thus, it can be effectively applied in deep space exploration. [ABSTRACT FROM AUTHOR]