VISUAL ENHANCEMENT FULL

We are experienced in working with children and adults with learning-related vision problems, computer vision syndrome, and the non-surgical treatment of strabismus (eye turns) and amblyopia ('lazy eye'). We will take both eye emergencies as well as scheduled appointments.

VISUAL ENHANCEMENT FULL

Vision Enhancement Center of Optometry is a full service eye and vision care provider. We invite you to browse our website to learn more about our optometry services, and invite you to join our patient family by scheduling an eye exam appointment at our Riverside office. Duong Nguyen and the Vision Enhancement Center of Optometry team strive to provide the finest in optometry services. Welcome to Vision Enhancement Center of Optometry in Riverside.

Furthermore, our algorithm has immediate applications in many areas such as virtual reality, immersive telepresence, autonomous driving, and video surveillance.Welcome to Vision Enhancement Center of Optometry

Our approach generates more temporally stable and visually pleasing results than existing video stitching approaches and commercial software. The stitched videos can be generated from an efficient pushbroom interpolation layer. We propose a pushbroom stitching network to learn dense flow fields to smoothly align the input videos. Inspired by the pushbroom cameras, we cast the stitching as a spatial interpolation problem. Third, we address the spatial-temporal enhancement problem for video stitching.

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. At test time, our model does not require computing optical flow and thus runs at 400+ FPS on GPU for high-resolution videos. 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. 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. Second, we address the temporal enhancement problem by learning the temporal consistency in videos. 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. Furthermore, we exploit the recursive learning technique to share parameters across and within pyramid levels to significantly reduce the model parameters. We train the proposed model with a multi-scale training, deep supervision, and robust loss functions to achieve state-of-the-art performance. Our model directly extracts features from input LR images and progressively reconstructs the sub-band residuals. 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.

We propose efficient algorithms based on deep convolutional neural networks for solving various visual enhancement problems.įirst, we address the problem of spatial enhancement for single-image super-resolution. In this thesis, we address the visual enhancement problems in three aspects, including the spatial, temporal, and the joint spatial-temporal domains. 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. However, obtaining high visual quality often comes with a cost of computational efficiency. Researchers have been actively working on this area due to its theoretical and practical interest. Visual enhancement is concerned with problems to improve the visual quality and viewing experience for images and videos.