AFD-Former: A Hybrid Transformer With Asymmetric Flow Division for Synthesized View Quality Enhancement

Recently, CNN-based post-processing has shown great potential in Synthesized View Quality Enhancement (SVQE). However, due to the limited receptive field of convolution, it is ineffective in explicitly modeling long-range dependencies, which are critical to eliminate the distortion induced by Depth Image Based Rendering (DIBR) in synthesized views. Although transformers exhibit tremendous success at learning global contextual information, it is weak at extracting local texture information. To take full advantages of the CNN and transformer, we present a novel U-shaped hybrid transformer with asymmetric flow division to collaboratively capture global-local information for SVQE, termed as AFD-former. Specifically, the AFD-former utilizes the Transformer-CNN Block (TCB) as encoder and decoder, in which several Dynamic Hybrid Attention Blocks (DHABs) are designed to simultaneously model long-range interactions and retain texture details. Then, considering that the deeper layers of the U-shaped network play more roles in capturing global information while shallow layers more in extracting local information, an Asymmetric Flow Division Unit (AFDU) is embedded into each DHAB to assign different contributions of global-local contextual information to the transformer and CNN branches across different layers. Finally, a dynamic learnable modulator is incorporated into two branches to help model effectively feature representation learning. That can be viewed as the dynamic process of adjusting the weight for each channel of the input feature based on contextual cues. Extensive experiments demonstrate that the proposed AFD-former can significantly enhance perceptual quality of synthesized views with similar SVQE speed compared with the related state-of-the-art SVQE methods. The source code will be available at https://github.com/House-yuyu/AFD-former .