Learning Spatial and Temporal Visual Enhancement

Visual enhancement is concerned with problems to improve the visual quality and viewing experience for images and videos. Researchers have been actively working on this area due to its theoretical and practical interest. However, obtaining high visual quality often comes with a cost of computational efficiency. With the growth of mobile applications and cloud services, it is crucial to develop effective and efficient algorithms for generating visually attractive images and videos. In this thesis, we address the visual enhancement problems in three aspects, including the spatial, temporal, and the joint spatial-temporal domains. We propose efficient algorithms based on deep convolutional neural networks for solving various visual enhancement problems.First, we address the problem of spatial enhancement for single-image super-resolution. We propose a deep Laplacian Pyramid Network to reconstruct a high-resolution image from an input low-resolution input in a coarse-to-fine manner. Our model directly extracts features from input LR images and progressively reconstructs the sub-band residuals. We train the proposed model with a multi-scale training, deep supervision, and robust loss functions to achieve state-of-the-art performance. Furthermore, we exploit the recursive learning technique to share parameters across and within pyramid levels to significantly reduce the model parameters. As most of the operations are performed on a low-resolution space, our model requires less memory and runs faster than state-of-the-art methods.Second, we address the temporal enhancement problem by learning the temporal consistency in videos. Given an input video and a per-frame processed video (processed by an existing image-based algorithm), we learn a recurrent network to reduce the temporal flickering and generate a temporally consistent video. We train the proposed network by minimizing both short-term and long-term temporal losses as well as a perceptual loss to strike a balance between temporal coherence and perceptual similarity with the processed frames. At test time, our model does not require computing optical flow and thus runs at 400+ FPS on GPU for high-resolution videos. Our model is task independent, where a single model can handle multiple and unseen tasks, including but not limited to artistic style transfer, enhancement, colorization, image-to-image translation and intrinsic image decomposition.Third, we address the spatial-temporal enhancement problem for video stitching. Inspired by the pushbroom cameras, we cast the stitching as a spatial interpolation problem. We propose a pushbroom stitching network to learn dense flow fields to smoothly align the input videos. The stitched videos can be generated from an efficient pushbroom interpolation layer. Our approach generates more temporally stable and visually pleasing results than existing video stitching approaches and commercial software. Furthermore, our algorithm has immediate applications in many areas such as virtual reality, immersive telepresence, autonomous driving, and video surveillance

Long Range Motion Estimation and Applications

Finding correspondences between images underlies many computer vision problems, such as op- tical flow, tracking, stereovision and alignment. Finding these correspondences involves formulating a matching function and optimizing it. This optimization process is often gradient descent, which avoids exhaustive search, but relies on the assumption of being in the basin of attraction of the right local minimum. This is often the case when the displacement is small, and current methods obtain very accurate results for small motions. However, when the motion is large and the matching function is abrupt this assumption is less likely to be true. One traditional way of avoiding this abruptness is to smooth the matching function spatially by blurring the images. As the displacement becomes larger, the amount of blur required to smooth the matching function becomes also larger. This averaging of pixels leads to a loss of detail in the image. Therefore, there is a trade-off between the size of the objects that can be tracked and the displacement that can be captured. In this thesis we address the basic problem of increasing the size of the basin of attraction in a matching function. We use an image descriptor called distribution fields (DFs). By blurring the images in DF space instead of in pixel space, we increase the size of the basin attraction with respect to traditional methods. We show competitive results using DFs both in object tracking and optical flow. Finally we demonstrate an application of capturing large motions for temporal video stitching

Exploiting Structural Constraints in Image Pairs

Ph.DDOCTOR OF PHILOSOPH

Light field image processing: an overview

Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data