Learning to See in the Dark

Imaging in low light is challenging due to low photon count and low SNR. Short-exposure images suffer from noise, while long exposure can induce blur and is often impractical. A variety of denoising, deblurring, and enhancement techniques have been proposed, but their effectiveness is limited in extreme conditions, such as video-rate imaging at night. To…Read More

Learning Category-Specific Mesh Reconstruction from Image Collections

Some truly remarkable results given the dataset from which this model was trained. Code on github will be available in the future. We present a learning framework for recovering the 3D shape, camera, and texture of an object from a single image. The shape is represented as a deformable 3D mesh model of an object…Read More

Accelerating Eulerian Fluid Simulation With Convolutional Networks

When asking those in the VFX industry which processes are the slowest and most compute-intensive fluid simulation is bound to be somewhere towards the top of the list. Physical phenomena such as fire and water are notoriously difficult to control, even in the hands of the most experienced artists, and usually require a significant number…Read More

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

Whilst the final image quality might not be quite yet there, there is surely more to come from this extremely promising area of research in the future. We present an end-to-end learning approach for motion deblurring, which is based on conditional GAN and content loss. It improves the state-of-the art in terms of peak signal-to-noise…Read More

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

The FlowNet demonstrated that optical flow estimation can be cast as a learning problem. However, the state of the art with regard to the quality of the flow has still been defined by traditional methods. Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods. In this paper, we advance the concept…Read More

HDR image reconstruction from a single exposure using deep CNNs

After creating LDR images by applying simulated camera sensor saturation to real HDR photos, the authors trained a model which could perform the inverse LDR->HDR operation and also generalize to previously unseen images. What’s more, they have released their dataset which can be downloaded from their project page. Camera sensors can only capture a limited…Read More

Machine Learning Labs

Machine learning in practice can be an arduous task. Managing multiple iterations of code, processing input data, feature engineering, training models, visualizing and tabulating results, performing analysis, and using experience to draw conclusions and adapt the system in a way which might yield improved results. In many cases you feel like you have more promising…Read More