Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for denoising images with different noise levels. They also lack flexibility to deal with spatially variant noise, limiting their applications in practical denoising. To address these issues, we present a fast and flexible denoising convolutional neural network, namely FFDNet, with a tunable noise level map as the input. The proposed FFDNet works on downsampled sub-images, achieving a good trade-off between inference speed and denoising performance. In contrast to the existing discriminative denoisers, FFDNet enjoys several desirable properties, including: 1) the ability to handle a wide range of noise levels (i.e., [0, 75]) effectively with a single network; 2) the ability to remove spatially variant noise by specifying a non-uniform noise level map; and 3) faster speed than benchmark BM3D even on CPU without sacrificing denoising performance. Extensive experiments on synthetic and real noisy images are conducted to evaluate FFDNet in comparison with state-of-the-art denoisers. The results show that FFDNet is effective and efficient, making it highly attractive for practical denoising applications.

FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising

Model-based optimization methods and discriminative learning methods have been the two dominant strategies for solving various inverse problems in low-level vision. Typically, those two kinds of methods have their respective merits and drawbacks, e.g., model-based optimization methods are flexible for handling different inverse problems but are usually time-consuming with sophisticated priors for the purpose of good performance, in the meanwhile, discriminative learning methods have fast testing speed but their application range is greatly restricted by the specialized task. Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e.g., deblurring). Such an integration induces considerable advantage when the denoiser is obtained via discriminative learning. However, the study of integration with fast discriminative denoiser prior is still lacking. To this end, this paper aims to train a set of fast and effective CNN (convolutional neural network) denoisers and integrate them into model-based optimization method to solve other inverse problems. Experimental results demonstrate that the learned set of denoisers can not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications.

/pdf/learning-deep-cnn-denoiser-prior-for-image-restoration-2yp6tg9rtn.pdf

Learning Deep CNN Denoiser Prior for Image Restoration

Model-based optimization methods and discriminative learning methods have been the two dominant strategies for solving various inverse problems in low-level vision. Typically, those two kinds of methods have their respective merits and drawbacks, e.g., model-based optimization methods are flexible for handling different inverse problems but are usually time-consuming with sophisticated priors for the purpose of good performance; in the meanwhile, discriminative learning methods have fast testing speed but their application range is greatly restricted by the specialized task. Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e.g., deblurring). Such an integration induces considerable advantage when the denoiser is obtained via discriminative learning. However, the study of integration with fast discriminative denoiser prior is still lacking. To this end, this paper aims to train a set of fast and effective CNN (convolutional neural network) denoisers and integrate them into model-based optimization method to solve other inverse problems. Experimental results demonstrate that the learned set of denoisers not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications.

Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for denoising images with different noise levels. They also lack flexibility to deal with spatially variant noise, limiting their applications in practical denoising. To address these issues, we present a fast and flexible denoising convolutional neural network, namely FFDNet, with a tunable noise level map as the input. The proposed FFDNet works on downsampled sub-images, achieving a good trade-off between inference speed and denoising performance. In contrast to the existing discriminative denoisers, FFDNet enjoys several desirable properties, including (i) the ability to handle a wide range of noise levels (i.e., [0, 75]) effectively with a single network, (ii) the ability to remove spatially variant noise by specifying a non-uniform noise level map, and (iii) faster speed than benchmark BM3D even on CPU without sacrificing denoising performance. Extensive experiments on synthetic and real noisy images are conducted to evaluate FFDNet in comparison with state-of-the-art denoisers. The results show that FFDNet is effective and efficient, making it highly attractive for practical denoising applications.

FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising

We present an end-to-end system for augmented and virtual reality telepresence, called Holoportation. Our system demonstrates high-quality, real-time 3D reconstructions of an entire space, including people, furniture and objects, using a set of new depth cameras. These 3D models can also be transmitted in real-time to remote users. This allows users wearing virtual or augmented reality displays to see, hear and interact with remote participants in 3D, almost as if they were present in the same physical space. From an audio-visual perspective, communicating and interacting with remote users edges closer to face-to-face communication. This paper describes the Holoportation technical system in full, its key interactive capabilities, the application scenarios it enables, and an initial qualitative study of using this new communication medium.

Holoportation: Virtual 3D Teleportation in Real-time

Conventional pipelines for capturing, displaying, and storing images are usually defined as a series of cascaded modules, each responsible for addressing a particular problem. While this divide-and-conquer approach offers many benefits, it also introduces a cumulative error, as each step in the pipeline only considers the output of the previous step, not the original sensor data. We propose an end-to-end system that is aware of the camera and image model, enforces natural-image priors, while jointly accounting for common image processing steps like demosaicking, denoising, deconvolution, and so forth, all directly in a given output representation (e.g., YUV, DCT). Our system is flexible and we demonstrate it on regular Bayer images as well as images from custom sensors. In all cases, we achieve large improvements in image quality and signal reconstruction compared to state-of-the-art techniques. Finally, we show that our approach is capable of very efficiently handling high-resolution images, making even mobile implementations feasible.

/pdf/flexisp-a-flexible-camera-image-processing-framework-26rswh6gfo.pdf

FlexISP: a flexible camera image processing framework

Video projectors are rapidly shrinking in size, power consumption, and cost. Such projectors provide unprecedented flexibility to stack, arrange, and aim pixels without the need for moving parts. We present a dense projector display that is optimized in size and resolution to display an autostereoscopic life-sized 3D human face with a wide 110 degree field of view. Applications include 3D teleconferencing and fully synthetic characters for education and interactive entertainment.

https://ict.usc.edu/pubs/An%20Autostereoscopic%20Projector%20Array%20Optimized%20for%203D%20Facial%20Display%20.pdf

An autostereoscopic projector array optimized for 3D facial display

We present a technique for achieving tracked vertical parallax for multiple users using a variety of autostereoscopic projector array setups, including front- and rear-projection and curved display surfaces. This hybrid parallax approach allows for immediate horizontal parallax as viewers move left and right and tracked parallax as they move up and down, allowing cues such as three-dimensional (3-D) perspective and eye contact to be conveyed faithfully. We use a low-cost RGB-depth sensor to simultaneously track multiple viewer head positions in 3-D space, and we interactively update the imagery sent to the array so that imagery directed to each viewer appears from a consistent and correct vertical perspective. Unlike previous work, we do not assume that the imagery sent to each projector in the array is rendered from a single vertical perspective. This lets us apply hybrid parallax to displays where a single projector forms parts of multiple viewers’ imagery. Thus, each individual projected image is rendered with multiple centers of projection, and might show an object from above on the left and from below on the right. We demonstrate this technique using a dense horizontal array of pico-projectors aimed into an anisotropic vertical diffusion screen, yielding 1.5 deg angular resolution over 110 deg field of view. To create a seamless viewing experience for multiple viewers, we smoothly interpolate the set of viewer heights and distances on a per-vertex basis across the array’s field of view, reducing image distortion, cross talk, and artifacts from tracking errors.

Interpolating vertical parallax for an autostereoscopic three-dimensional projector array

3D displays are increasingly popular in consumer and commercial applications. Many such displays show 3D images to viewers wearing special glasses, while showing an incomprehensible double image to viewers without glasses. We demonstrate a simple method that provides those with glasses a 3D experience, while viewers without glasses see a 2D image without artifacts.In addition to separate left and right images in each frame, we add a third image, invisible to those with glasses. In the combined view seen by those without glasses, this cancels the right image, leaving only the left.If the left and right images are of equal brightness, this approach results in low contrast for viewers without glasses. Allowing differential brightness between the left and right images improves 2D contrast. We observe experimentally that: (1) viewers without glasses prefer our 3Dp2DTV to a standard 3DTV, (2) viewers with glasses maintain a strong 3D percept, even when one eye is significantly darker than the other, and (3) sequential-stereo display viewers with glasses experience a depth illusion caused by the Pulfrich effect, but it is small and innocuous.Our technique is applicable to displays using either active shutter glasses or passive glasses. Our prototype uses active shutter glasses and a polarizer.

3D+2DTV: 3D displays with no ghosting for viewers without glasses

The emergence of high frame rate computational displays has created an opportunity for viewing experiences impossible on traditional displays. These displays can create views personalized to multiple users, encode hidden messages, or even decompose and encode a targeted light field to create glasses-free 3D views [Masia et al. 2013].

Jing Liu

Papers

FlexISP: a flexible camera image processing framework

An autostereoscopic projector array optimized for 3D facial display

Interpolating vertical parallax for an autostereoscopic three-dimensional projector array

3D+2DTV: 3D displays with no ghosting for viewers without glasses

When does the hidden butterfly not flicker