Figure 1: Two views of the graph of the same edge-avoiding wavelet centered at the shoulder of the Cameraman. The support of the wavelet is confined within the limits set by the strong edges around the upper body. We propose a new family of second-generation wavelets constructed using a robust data-prediction lifting scheme. The support of these new wavelets is constructed based on the edge content of the image and avoids having pixels from both sides of an edge. Multi-resolution analysis, based on these new edge-avoiding wavelets, shows a better decorrelation of the data compared to common linear translation-invariant multi-resolution analyses. The reduced inter-scale correlation allows us to avoid halo artifacts in band-independent multi-scale processing without taking any special precautions. We thus achieve nonlinear data-dependent multiscale edge-preserving image filtering and processing at computation times which are linear in the number of image pixels. The new wavelets encode, in their shape, the smoothness information of the image at every scale. We use this to derive a new edge-aware interpolation scheme that achieves results, previously computed by solving an inhomogeneous Laplace equation, through an explicit computation. We thus avoid the difficulties in solving large and poorly-conditioned systems of equations. We demonstrate the effectiveness of the new wavelet basis for various computational photography applications such as multi-scale dynamic-range compression, edge-preserving smoothing and detail enhancement, and image colorization.

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,

Figure 1: We seek to recolor the input image (a). However, changing the color (reflectance) of the shirt alone, without modifying the illumination, does not account for the correct diffuse reflection on the girl’s arm or interreflections in the fine texture of the shirt (b). Indeed, the image in (b) still has bluish reflections on the arm and a purple color shift on the shirt. Our user-assisted decomposition (Figure 2) lets us modify indirect illumination to match the modified shirt color (c), leading to a much more consistent and natural looking recoloring. Changing the color of an object is a basic image editing operation, but a high quality result must also preserve natural shading. A common approach is to first compute reflectance and illumination intrinsic images. Reflectances can then be edited independently, and recomposed with the illumination. However, manipulating only the reflectance color does not account for diffuse interreflections, and can result in inconsistent shading in the edited image. We propose an approach for further decomposing illumination into direct lighting, and indirect diffuse illumination from each material. This decomposition allows us to change indirect illumination from an individual material independently, so it matches the modified reflectance color. To address the underconstrained problem of decomposing illumination into multiple components, we take advantage of its smooth nature, as well as user-provided constraints. We demonstrate our approach on a number of examples, where we consistently edit material colors and the associated interreflections. Links: DL PDF WEB 1

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.