Efficient Deep Image Prior with Spatial-Channel Attention Transformer
The Deep Image Prior(DIP) suggests that it is possible to train a randomly initialized network with a suitable architecture to solve the inverse imaging problem by simply optimizing its parameters to reconstruct a single degraded image. However, the learning effect it seeks is often achieved with the most naive local convolution, which inevitably leads to the inverse imaging problem being limited by the model’s generative ability. Furthermore, image info is often not related to surrounding pixels but to overall color and spatial info. Simple local convolution in inverse imaging can’t capture precise details. Moreover, DIP is an unsupervised process but requires iterations to learn inverse imaging, consuming computational power and limiting adaption of global attention. To solve these problems, this article explores the possibility of globalizing the DIP task’s learning and introducing tri-directional multi-head self-attention to optimize the computation consumption brought by pixel-level attention. Our observations found that global learning can effectively enhance the detail information of edge pixels, making images more vivid and textures clearer. In addition, tri-directional multi-head self-attention can efficiently replace the global perception ability of pixel-level self-attention. Finally, we demonstrate that global learning can effectively improve the imaging effect of inverse imaging problems and enhance the information of texture edge pixels. Moreover, tri-directional multi-head self-attention can effectively alleviate the computation redundancy of pixel-level self-attention, thus achieving efficient and high-quality inverse imaging tasks. The principles of this approach—global feature capture and efficient attention modeling—extend its potential applicability beyond imaging to domains such as software security. For instance, it can enhance tasks like vulnerability analysis by reconstructing obscured code patterns and improve threat modeling through efficient correlation of multi-dimensional attack vectors, balancing detail fidelity with computational practicality.
