• June 2020 -- We have 3 papers in CVPR 2020 -- two regular conference and one workshop. Here are the summaries and links to relevant material are below.

    • "GIFnets: Differentiable GIF Encoding Framework": We introduce (to our knowledge), the first differentiable GIF encoding pipeline. It includes three novel neural networks: PaletteNet, DitherNet, and BandingNet. Each provides an important functionality within the GIF encoding pipeline. PaletteNet predicts a near-optimal color palette given an input image. DitherNet manipulates the input image to reduce color banding artifacts and provides an alternative to traditional dithering. Finally, BandingNet is designed to detect color banding, and provides a new perceptual loss specifically for GIF images.

    • "Distortion Agnostic Deep Watermarking": We develop a framework for distortion-agnostic watermarking, where the image distortion is not explicitly modeled during training. Instead, the robustness of our system comes from two sources: adversarial training and channel coding. Compared to training on a fixed set of distortions and noise levels, our method achieves comparable or better results on distortions available during training, and better performance overall on unknown distortions.

    • "LIDIA: Lightweight Learned Image Denoising with Instance Adaptation": We use a combination of supervised and unsupervised training, where the first stage gets a general denoiser and the second does instance adaptation. LIDIA produces near state-of-the-art quality, while having relatively very small number of parameters as compared to the leading methods

  • July 2019 -- Our paper on Handheld Multi-frame Super-resolution was presented at SIGGRAPH 2019. You can find our paper, supplementary material and a short video describing the work at the project website. This technology powers the Super-Res Zoom and Night Sight (merge) features on Pixel phones.