LDINet: Latent decomposition-interpolation for single image fast-moving objects deblatting

Abstract

The image of fast-moving objects (FMOs) usually contains a blur stripe indicating the blurred object that is mixed with the background. In this work we propose a novel Latent Decomposition-Interpolation Network (LDINet) to generate the appearances and shapes of the objects from the blurry stripe contained in the single image. In particular, we introduce an Decomposition-Interpolation Module (DIM) to break down the feature maps of the inputs into discrete time indexed parts and interpolate the target latent frames according to the provided time indexes with affine transformations, where the features are categorized into the scalar-like and gradient-like parts when warping in the interpolation. Finally, a decoder renders the prediction results. In addition, based on the results, a Refining Conditional Deblatting (RCD) approach is presented to further enhance the fidelity. Extensive experiments are conducted and have shown that the proposed methods achieve superior performances compared to the existing competing methods.