Abstract. In this paper, we propose a novel type of explicit image filter- guided filter. Derived from a local linear model, the guided filter generates the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided filter can perform as an edge-preserving smoothing operator like the popular bilateral filter [1], but has better behavior near the edges. It also has a theoretical connection with the matting Laplacian matrix [2], so is a more generic concept than a smoothing operator and can better utilize the structures in the guidance image. Moreover, the guidedfilterhasafastandnon-approximatelinear-time algorithm, whose computational complexity is independent of the filtering kernel size. We demonstrate that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications including noise reduction, detail smoothing/enhancement, HDR compression, image matting/feathering, haze removal, and joint upsampling. 1

Figure 1: Affinity-based edit propagation methods such as [An and Pellacini 2008] allow one to change the appearance of an image or video (e.g., the color of the bird here) using only a few strokes, yet consuming prohibitive amount of time and memory for large data (e.g., 48 minutes and 23GB for this video containing 61M pixels). Our approximation scheme drastically reduces the cost of edit propagation methods (to 8 seconds and 22MB in this example) by exploring adaptive clustering in the affinity space. Video courtesy of BBC Motion Gallery (UK). Image/video editing by strokes has become increasingly popular due to the ease of interaction. Propagating the user inputs to the rest of the image/video, however, is often time and memory consuming especially for large data. We propose here an efficient scheme that allows affinity-based edit propagation to be computed on data containing tens of millions of pixels at interactive rate (in matter of seconds). The key in our scheme is a novel means for approximately solving the optimization problem involved in edit propagation, using adaptive clustering in a high-dimensional, affinity space. Our approximation significantly reduces the cost of existing affinitybased propagation methods while maintaining visual fidelity, and enables interactive stroke-based editing even on high resolution images and long video sequences using commodity computers. 1

(a) input HDR image tone-mapped with a simple gamma curve (details are compressed) (b) our pyramid-based tone mapping, set to preserve details without increasing them (c) our pyramid-based tone mapping, set to strongly enhance the contrast of details Figure 1: We demonstrate edge-aware image filters based on the direct manipulation of Laplacian pyramids. Our approach produces highquality results, without degrading edges or introducing halos, even at extreme settings. Our approach builds upon standard image pyramids and enables a broad range of effects via simple point-wise nonlinearities (shown in corners). For an example image (a), we show results of tone mapping using our method, creating a natural rendition (b) and a more exaggerated look that enhances details as well (c). Laplacian pyramids have previously been considered unsuitable for such tasks, but our approach shows otherwise. The Laplacian pyramid is ubiquitous for decomposing images into multiple scales and is widely used for image analysis. However, because it is constructed with spatially invariant Gaussian kernels,

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The ability to quickly and intuitively edit digital contents has become increasingly important in our everyday life. We propose a novel method for propagating a sparse set of user edits (e.g., changes in color, brightness, contrast, etc.) expressed as casual strokes to nearby regions in an image or video with similar appearances. Existing methods for edit propagation are typically based on optimization, whose computational cost can be prohibitive for large inputs. We re-formulate propagation as a function interpolation problem in a high-dimensional space, which we solve very efficiently using radial basis functions. While simple to implement, our method significantly improves the speed and space cost of existing methods, and provides instant feedback of propagation results even on large images and videos.

In this paper we present a new algorithm to transform an RGB color image to a grayscale image. We propose using non-linear dimension reduction techniques to map higher dimensional color vectors to lower dimensional ones. This approach generalizes the gradient domain manipulation for high dimensional images. Our experiments show that the proposed algorithm generates competitive results and reaches a good compromise between quality and speed.

We describe a set of image editing and viewing tools that explicitly take into account the resolution of the display on which the image is viewed. Our approach is twofold. First, we design editing tools that process only the visible data, which is useful for images larger than the display. This encompasses cases such as multi-image panoramas and highresolution medical data. Second, we propose an adaptive way to set viewing parameters such brightness and contrast. Because we deal with very large images, different locations and scales often require different viewing parameters. We let users set these parameters at a few places and interpolate satisfying values everywhere else. We demonstrate the efficiency of our approach on different display and image sizes. Since the computational complexity to render a view depends on the display resolution and not the actual input image resolution, we achieve interactive image editing even on a 16 gigapixel image. 1.

frame n 4 frame n-0.5 frame n-1 Figure 1: Remote rendering allows navigating in complex scenes even on weak client hardware. But not only final images are of interest on the client side, auxiliary information like depth or motion become increasingly attractive in this context for various purposes. Examples include spatio-temporal upsampling (1, 2), 3D stereo rendering (3), or frame extrapolation (4). Standard encoders (H.264 in image 1) are currently not always well-adapted to such streams and our contribution is a novel method to efficiently encode and decode augmented video streams with high-quality (compare insets in image 1 and 2). In this paper, we focus on efficient compression and streaming of frames rendered from a dynamic 3D model. Remote rendering and on-the-fly streaming become increasingly attractive for interactive applications. Data is kept confidential and only images are sent to the client. Even if the client’s hardware resources are modest, the user can interact with state-of-the-art rendering applications executed on the server. Our solution focuses on augmented video information, e.g., by depth, which is key to increase robustness with respect to data loss, image reconstruction,

Figure 1: A variety of effects illustrating the versatility of our domain transform and edge-preserving filters applied to the photograph in (a). We present a new approach for performing high-quality edgepreserving filtering of images and videos in real time. Our solution is based on a transform that defines an isometry between curves on the 2D image manifold in 5D and the real line. This transform preserves the geodesic distance between points on these curves, adaptively warping the input signal so that 1D edge-preserving filtering can be efficiently performed in linear time. We demonstrate three realizations of 1D edge-preserving filters, show how to produce high-quality 2D edge-preserving filters by iterating 1D-filtering operations, and empirically analyze the convergence of this process. Our approach has several desirable features: the use of 1D operations leads to considerable speedups over existing techniques and potential memory savings; its computational cost is not affected by the choice of the filter parameters; and it is the first edge-preserving filter to work on color images at arbitrary scales in real time, without resorting to subsampling or quantization. We demonstrate the versatility of our domain transform and edge-preserving filters on several real-time image and video processing tasks including edgepreserving filtering, depth-of-field effects, stylization, recoloring, colorization, detail enhancement, and tone mapping.

Abstract As one of the most fundamental operations in computer graphics and computer vision, sharpness enhancement can enhance an image in respect of sharpness characteristics. Unfortunately, the prevalent methods often fail to eliminate image noise, unrealistic details, or incoherent enhancement. In this paper, we propose a new sharpness enhancement approach that can boost the sharpness characteristics of an image effectively with affinity-based edge preserving. Our approach includes three gradient-domain operations: sharpness saliency representation, affinity-based gradient transformation, and gradient-domain image reconstruction. Moreover, we also propose an evaluation method based on sharpness distribution for analyzing all sharpness enhancement approaches in respect of sharpness characteristics. By evaluating the sharpness distribution and comparing the visual appearance, we demonstrate the effectiveness of our sharpness enhancement approach. Keywords Sharpness enhancement · Gradient-domain filter · Image sharpening