Enhancing seismic image resolution while effectively suppressing noise remains a critical challenge in accurately characterizing subsurface geological structures for oil and gas exploration. Traditional methods often fail to balance the recovery of fine details with robustness to noise, particularly in complex geological settings or under high-noise conditions. This study proposes a deep learning-based joint model, U-Net Shifted Window (Swin) Transformer-based dense residual network (U-STDRNet). The model integrates the global modeling capability of the Swin Transformer, the hierarchical feature reuse mechanism of the residual dense network, and an attention-guided strategy to jointly perform seismic image super-resolution and denoising. Built upon the U-Net encoder-decoder architecture, the model embeds Swin Transformer-based convolutional residual blocks. These blocks employ both a feature fusion block with the Swin Transformer and a feature fusion block with a convolutional neural network to effectively capture stratigraphic continuity and enhance detailed features such as fault edges. Residual dense blocks further improve weak signal recovery (e.g., thin-layer interfaces) through dense residual connections. Furthermore, the convolutional block attention module is integrated into skip connections, employing a dual-channel spatial weighting mechanism to suppress noise and emphasize key geological regions. Experimental results and field-data experiments demonstrate that U-STDRNet achieves a higher peak signal-to-noise ratio than the traditional U-Net. In addition, the model successfully restores fault and fold continuity details while exhibiting superior noise suppression compared to existing methods.