We propose to address the Inverse Halftone Colorization task, which tries to recover colorful images from black and white halftone prints, and can be treated as the joint problem of inverse halftone and colorization. Although the inverse halftone colorization seems to be achievable via applying inverse halftone followed by colorization, our proposed method advances from two perspectives: (1) we empirically discover that the orders of cascading inverse halftone and colorization (i.e first inverse halftone then colorization versus the reverse order) would lead to results with different properties, hence a fusion scheme is proposed to integrate their results; (2) we introduce several novel losses to encourage the realness, diversity, and the structural coherence of the colorization. Moreover, our model is flexible to support both exemplar-based and random colorization. We conduct extensive experiments to demonstrate the efficacy of our method as well as verify the contributions of our design choices.

Removing undesired reflection from an image captured through a glass window is a notable task in computer vision. In this paper, we propose a novel model with auxiliary techniques to tackle the problem of single image reflection removal. Our model takes a reflection contaminated image as input, and decomposes it into the reflection layer and the transmission layer. In order to ensure quality of the transmission layer, we introduce three auxiliary techniques into our architecture, including the edge guidance, a reflection classifier, and the recurrent decomposition. The contributions and the efficacy of these techniques are investigated and verified in the ablation study. Furthermore, in comparison to the state-of-the-art baselines of reflection removal, both quantitative and qualitative results demonstrate that our proposed method is able to deal with different kinds of images, achieving the best results in average.

Time-lapse videos usually perform eye-catching appearances but are often hard to create. In this paper, we propose a self-supervised end-to-end model to generate the time-lapse video from a single image and a reference video. Our key idea is to extract both the style and the features of temporal variation from the reference video, and transfer them onto the input image. To ensure both the temporal consistency and realness of our resultant videos, we introduce several novel designs in our architecture, including classwise NoiseAdaIN, flow loss, and the video discriminator. In comparison to the baselines of state-of-the-art style transfer approaches, our proposed method is not only efficient in computation but also able to create more realistic and temporally smooth time-lapse video of a still image, with its temporal variation consistent to the reference.