Defocus Magnification

A blurry background due to shallow depth of field is often desired for photographs such as portraits, but, unfortunately, small point-and-shoot cameras do not permit enough defocus because of the small diameter of their lenses. We present an image-processing technique that increases the defocus in an image to simulate the shallow depth of field of a lens with a larger aperture. Our technique estimates the spatially-varying amount of blur over the image, and then uses a simple image-based technique to increase defocus. We first estimate the size of the blur kernel at edges and then propagate this defocus measure over the image. Using our defocus map, we magnify the existing blurriness, which means that we blur blurry regions and keep sharp regions sharp. In contrast to more difficult problems such as depth from defocus, we do not require precise depth estimation and do not need to disambiguate textureless regions.     

Single‐shot High Dynamic Range Imaging Using Coded Electronic Shutter

Typical high dynamic range (HDR) imaging approaches based on multiple images have difficulties in handling moving objects and camera shakes, suffering from the ghosting effect and the loss of sharpness in the output HDR image. While there exist a variety of solutions for resolving such limitations, most of the existing algorithms are susceptible to complex motions, saturation, and occlusions. In this paper, we propose an HDR imaging approach using the coded electronic shutter which can capture a scene with row‐wise varying exposures in a single image. Our approach enables a direct extension of the dynamic range of the captured image without using multiple images, by photometrically calibrating rows with different exposures. Due to the concurrent capture of multiple exposures, misalignments of moving objects are naturally avoided with significant reduction in the ghosting effect. To handle the issues with under‐/over‐exposure, noise, and blurs, we present a coherent HDR imaging process where the problems are resolved one by one at each step. Experimental results with real photographs, captured using a coded electronic shutter, demonstrate that our method produces a high quality HDR images without the ghosting and blur artifacts.     

Perception of Visual Artifacts in Image‐Based Rendering of Façades

Image‐based rendering (IBR) techniques allow users to create interactive 3D visualizations of scenes by taking a few snapshots. However, despite substantial progress in the field, the main barrier to better quality and more efficient IBR visualizations are several types of common, visually objectionable artifacts. These occur when scene geometry is approximate or viewpoints differ from the original shots, leading to parallax distortions, blurring, ghosting and popping errors that detract from the appearance of the scene. We argue that a better understanding of the causes and perceptual impact of these artifacts is the key to improving IBR methods. In this study we present a series of psychophysical experiments in which we systematically map out the perception of artifacts in IBR visualizations of façades as a function of the most common causes. We separate artifacts into different classes and measure how they impact visual appearance as a function of the number of images available, the geometry of the scene and the viewpoint. The results reveal a number of counter‐intuitive effects in the perception of artifacts. We summarize our results in terms of practical guidelines for improving existing and future IBR techniques.     

基于稀疏表示和结构自相似性的单幅图像盲解卷积算法

图像盲解卷积研究当模糊核未知时,如何从模糊图像复原出原始清晰图像.由于盲解卷积是一个欠定问题,现有的盲解卷积算法都直接或间接地利用各种先验知识.本文提出了一种结合稀疏表示与结构自相似性的单幅图像盲解卷积算法,该算法将图像的稀疏性先验和结构自相似性先验作为正则化约束加入到图像盲解卷积的目标函数中,并利用图像不同尺度间的结构自相似性,将观测模糊图像的降采样图像作为稀疏表示字典的训练样本,保证清晰图像在该字典下的稀疏性.最后利用交替求解的方式估计模糊核和清晰图像.模拟和真实数据上的实验表明本文算法能够准确估计模糊核,复原清晰的图像边缘,并具有很好的鲁棒性.     

View and Time Interpolation in Image Space

The ability to interpolate between images taken at different time and viewpoints directly in image space opens up new possiblities. The goal of our work is to create plausible in‐between images in real time without the need for an intermediate 3D reconstruction. This enables us to also interpolate between images recorded with uncalibrated and unsynchronized cameras. In our approach we use a novel discontiniuity preserving image deformation model to robustly estimate dense correspondences based on local homographies. Once correspondences have been computed we are able to render plausible in‐between images in real time while properly handling occlusions. We discuss the relation of our approach to human motion perception and other image interpolation techniques.     

Creating Fluid Animation from a Single Image using Video Database

We present a method for synthesizing fluid animation from a single image, using a fluid video database. The user inputs a target painting or photograph of a fluid scene along with its alpha matte that extracts the fluid region of interest in the scene. Our approach allows the user to generate a fluid animation from the input image and to enter a few additional commands about fluid orientation or speed. Employing the database of fluid examples, the core algorithm in our method then automatically assigns fluid videos for each part of the target image. Our method can therefore deal with various paintings and photographs of a river, waterfall, fire, and smoke. The resulting animations demonstrate that our method is more powerful and efficient than our prior work.     

Seamless Video Stitching from Hand‐held Camera Inputs

Images/videos captured by portable devices (e.g., cellphones, DV cameras) often have limited fields of view. Image stitching, also referred to as mosaics or panorama, can produce a wide angle image by compositing several photographs together. Although various methods have been developed for image stitching in recent years, few works address the video stitching problem. In this paper, we present the first system to stitch videos captured by hand‐held cameras. We first recover the 3D camera paths and a sparse set of 3D scene points using CoSLAM system, and densely reconstruct the 3D scene in the overlapping regions. Then, we generate a smooth virtual camera path, which stays in the middle of the original paths. Finally, the stitched video is synthesized along the virtual path as if it was taken from this new trajectory. The warping required for the stitching is obtained by optimizing over both temporal stability and alignment quality, while leveraging on 3D information at our disposal. The experiments show that our method can produce high quality stitching results for various challenging scenarios.     

Real‐time Depth of Field Rendering via Dynamic Light Field Generation and Filtering

We present a new algorithm for efficient rendering of high‐quality depth‐of‐field (DoF) effects. We start with a single rasterized view (reference view) of the scene, and sample the light field by warping the reference view to nearby views. We implement the algorithm using NVIDIA's CUDA to achieve parallel processing, and exploit the atomic operations to resolve visibility when multiple pixels warp to the same image location. We then directly synthesize DoF effects from the sampled light field. To reduce aliasing artifacts, we propose an image‐space filtering technique that compensates for spatial undersampling using MIP mapping. The main advantages of our algorithm are its simplicity and generality. We demonstrate interactive rendering of DoF effects in several complex scenes. Compared to existing methods, ours does not require ray tracing and hence scales well with scene complexity.     

Motion Deblurring from a Single Image using Circular Sensor Motion

Image blur caused by object motion attenuates high frequency content of images, making post‐capture deblurring an ill‐posed problem. The recoverable frequency band quickly becomes narrower for faster object motion as high frequencies are severely attenuated and virtually lost. This paper proposes to translate a camera sensor circularly about the optical axis during exposure, so that high frequencies can be preserved for a wide range of in‐plane linear object motion in any direction within some predetermined speed. That is, although no object may be photographed sharply at capture time, differently moving objects captured in a single image can be deconvolved with similar quality. In addition, circular sensor motion is shown to facilitate blur estimation thanks to distinct frequency zero patterns of the resulting motion blur point‐spread functions. An analysis of the frequency characteristics of circular sensor motion in relation to linear object motion is presented, along with deconvolution results for photographs captured with a prototype camera.     

Depth-of-Field Rendering by Pyramidal Image Processing

We present an image-based algorithm for interactive rendering depth-of-field effects in images with depth maps. While previously published methods for interactive depth-of-field rendering suffer from various rendering artifacts such as color bleeding and sharpened or darkened silhouettes, our algorithm achieves a significantly improved image quality by employing recently proposed GPU-based pyramid methods for image blurring and pixel disocclusion. Due to the same reason, our algorithm offers an interactive rendering performance on modern GPUs and is suitable for real-time rendering for small circles of confusion. We validate the image quality provided by our algorithm by side-by-side comparisons with results obtained by distributed ray tracing.