Abstract Abstract—Super-resolution image reconstruction produces a high-resolution image from a set of shifted, blurred, and decimated versions thereof.
We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks conditional GANs.
Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic. In this work, we generate x visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures.
Furthermore, we extend our framework to interactive visual manipulation with two additional features. Second, we propose a method to generate diverse results given the same input, allowing users to edit the object appearance interactively. Human opinion studies demonstrate that our method significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing.
He received his Ph. His street scene understanding paper was selected in the best paper finalist by the Robotics Science and System RSS conference in Recently, his research focus shifted to generative models for image understanding and generation.
His goal is to enable machines superhuman-like imagination capabilities. Depth data is indispensable for reconstructing or understanding 3D scenes.
It serves as a key ingredient for applications such as synthetic defocus, autonomous driving, and augmented reality. Although active 3D sensors e. However, estimating depth from images is inherently under-determined, to regularize the problem, one typically needs handcrafted models characterizing the properties of depth data or scene geometry.
As the recent advances in deep learning, depth estimation is cast as a learning task, leading to state-of-the-art performance. In this talk, I will present our new progress on depth estimation with convolutional neural networks CNN.
Particularly, I will first introduce cascade residual learning CRLour two-stage deep architecture on stereo matching producing high-quality disparity estimates. Observations with CRL inspires us to propose a domain-adaptation approachzoom and learn ZOLE for training a deep stereo matching algorithm without the ground-truth data of a target domain.
By combining a view synthesis network and the first stage of CRL, we propose single view stereo matching SVS for single image depth estimation, with a performance superior to the classic stereo block matching method taking two images as inputs. He has published more than papers in top-tier academic journals and conferences.
He is a Fellow of IET. Copying and editing images Abstract: He was the Associate Department Head from - His current research interests include machine learning applied to computer vision, Bayesian models of visual perception, and computational photography.
He received outstanding paper awards at computer vision or machine learning conferences in, andand test-of-time retrospective awards for papers from, and Previous research topics include steerable filters and pyramids, orientation histograms, the generic viewpoint assumption, color constancy, computer vision for computer games, and belief propagation in networks with loops.
He is active in the program or organizing committees of computer vision, graphics, and machine learning conferences. Xian-Sheng Hua Alibaba Title: Massive data is accumulating in a city everyday, and the problem is how to explore the potential value of the data.
Computer vision technologies are widely applied in City Brain, such as image classification, object detection, multi-object tracking, semantic segmentation, image retrieval, as well as low-level image processing such as super-resolution and image denoising.
In this talk, we will show how these technologies applied to City Brain.
Before that, he was a Senior Researcher in Microsoft Research. In this talk I review the ideas behind Metamer Sets. Given knowledge of the spectral sensitivities of the camera and the spectral power distribution of the illuminant we show how to recover the Metamer Set for an RGB.
Calculating Metamer Sets is not easy, so I will present new work that reformulates the problem to allow faster computation.
He joined UEA in when he was awarded a full professorship at the age of He was and remains the youngest ever professorial appointment at that institution. Significantly, Graham was the first academic at UEA in its 50 year history to either raise venture capital investment for a spin-out company — Imsense Ltd developed technology to make pictures look better - or to make a money for the university when this company was subsequently sold to a blue chip industry major in He is interested in taking the creative spark of an idea, developing the underlying theory and algorithms and then implementing and commercialising the technology.
Graham's IP ships in s of millions of products. In the Royal Photographic Society presented graham with the Davies medal in recognition of his contributions to the Photographic Industry. The RPS made Graham a fellow in In recognition of distinguished service to the Society for Imaging Science and Technology, Graham was elected a fellow of that society in Super-Resolution Image Reconstruction: A Technical Overview I n most electronic imaging applications, images with high resolution (HR) are desired and often required.
HR means that pixel density within an image is high, and therefore an HR image can offer more details that may be critical in various applications. For example, HR medical . Super-Resolution-Image-Reconstruction Aim to generate high resolution image using series of low resolution images.
I have already used traditional method, . Super Resolution image reconstruction is a promising technique of digital from MI IP at Universidad del Rosario. Multi-frame Super-resolution • How can we obtain a HR image from multiple LR images? – Basic premise is the availability of multiple LR image captured form the same scene.
– These multiple LR images provide different “looks” at the same scene. Its temporal resolution is limited by the maximal emitter density that can be handled by the image reconstruction algorithms. Multiple algorithms have been developed to accurately locate the emitters even when they have significant overlaps.
Image Super-resolution, Reconstruction, Algorithm, Modified Sparse Representation, Image Processing. 1 Introduction.
Image super-resolution refers to the use of one or more of low resolution image, as through the corresponding algorithm to obtain the clear image of the high resolution image.