RISTRA: Recursive Image Super-resolution Transformer with Relativistic Assessment
IEEE Transactions on Multimedia(2024)
摘要
Many recent image restoration methods use Transformer as the backbone network and redesign the Transformer blocks. Differently, we explore the parameter-sharing mechanism over Transformer blocks and propose a dynamic recursive process to address the image super-resolution task efficiently. We firstly present a Recursive Image Super-resolution Transformer (RIST). By sharing the weights across different blocks, a plain forward process through the whole Transformer network can be folded into recursive iterations through a Transformer block. Such a parameter-sharing based recursive process can not only reduce the model size greatly, but also enable restoring images progressively. Features in the recursive process are modeled as a sequence and propagated with a temporal attention network. Besides, by analyzing the prediction variation across different iterations in RIST, we design a dynamic recursive process that can allocate adaptive computation costs to different samples. Specifically, a quality assessment network estimates the restoration quality and terminates the recursive process dynamically. We propose a relativistic learning strategy to simplify the objective from absolute image quality assessment to relativistic quality comparison. The proposed Recursive Image Super-resolution Transformer with Relativistic Assessment (RISTRA) reduces the model size greatly with the parameter-sharing mechanism, and achieves an instance-wise dynamic restoration process as well. Extensive experiments on several image super-resolution benchmarks show the superiority of our approach over state-of-the-art counterparts
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关键词
Super Resolution,Vision Transformer,Parameter Sharing
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