Figure 1: Dehazing based on a single input image and the corresponding depth estimate. In this paper we present a new method for estimating the optical transmission in hazy scenes given a single input image. Based on this estimation, the scattered light is eliminated to increase scene visibility and recover haze-free scene contrasts. In this new approach we formulate a refined image formation model that accounts for surface shading in addition to the transmission function. This allows us to resolve ambiguities in the data by searching for a solution in which the resulting shading and transmission functions are locally statistically uncorrelated. A similar principle is used to estimate the color of the haze. Results demonstrate the new method abilities to remove the haze layer as well as provide a reliable transmission estimate which can be used for additional applications such as image refocusing and novel view synthesis.

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.

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

In landscape photography, distant objects often appear blurred with a blue color cast, a degradation caused by atmospheric haze. To enhance image contrast, pleasantness and information content, dehazing can be performed. We propose that fusing a visible and an near-infrared (NIR) image of the same scene results in a dehazed color image without the need for haze or airlight detection or the generation of depth maps. This is achieved through a multiresolution approach using edge-preserving filtering to minimize artifacts. The near-infrared part of the spectrum is easy to acquire with normal digital cameras. The NIR images are generally devoid of haze as it is an inherent function of the wavelengths. Experiments on real images validate our approach.

In a wide range of applications, we often wish to quickly inspect and visualize historically significant and highly detailed objects. For such scientific illustration applications, often the focus is on high-frequency surface details and on conveying important shape and feature information. In our work, we provide a complete system to visualize objects on the spot. Our approach uses photometric and geometric processing, combined with a set of visualization methods tuned to the interactive inspection and analysis of objects. Highly accurate models are acquired in about 30 seconds using an uncalibrated setup, obtaining both detailed surface geometry and detailed surface normal information. Subsequently, captured objects are visually tracked enabling hand-held manipulation and visualization. In this paper, we demonstrate our system using several real-world objects.

Figure 1: Our multiscale decomposition of image (a) allows detail to be extracted based on spatial scale rather than contrast and preserves edges. (b) Boosting fine scale features increases the contrast of the pattern on the vase. (c) Boosting coarse scale contrast and suppressing fine features reduces the contrast of the pattern, while increasing the contrast of the vase with its background. (d) Scanline plots (rows indicated using arrows in (a), (b) and (c)), illustrating the effect of the two equalizations (b) and (c). The dashed lines in the plots show two examples of edges that have been preserved. We propose a new model for detail that inherently captures oscillations, a key property that distinguishes textures from individual edges. Inspired by techniques in empirical data analysis and morphological image analysis, we use the local extrema of the input image to extract information about oscillations: We define detail as oscillations between local minima and maxima. Building on the key observation that the spatial scale of oscillations are characterized by the density of local extrema, we develop an algorithm for decomposing images into multiple scales of superposed oscillations. Current edge-preserving image decompositions assume image detail to be low contrast variation. Consequently they apply filters that extract features with increasing contrast as successive layers of detail. As a result, they are unable to distinguish between highcontrast, fine-scale features and edges of similar contrast that are to be preserved.We compare our results with existing edge-preserving image decomposition algorithms and demonstrate exciting applications that are made possible by our new notion of detail.

Figure 1: In traditional image compositing (a) a user applies geometric transformations to a source image (top) and inserts it into a target image (bottom). Tools such as the Photoshop Healing Brush use gradient domain compositing to ensure that the composite is seamless (b) but the inconsistencies between the two images, make the result look unrealistic: the inserted face is much smoother than the rest of the image. Our method “harmonizes ” the images before blending them, producing a composite that is seamless and realistic (c). The close-up images (d) compare traditional gradient-domain blending (top) to the harmonized result (bottom). Traditional image compositing techniques, such as alpha matting and gradient domain compositing, are used to create composites that have plausible boundaries. But when applied to images taken from different sources or shot under different conditions, these techniques can produce unrealistic results. In this work, we present a framework that explicitly matches the visual appearance of images through a process we call image harmonization, before blending them. At the heart of this framework is a multi-scale technique that allows us to transfer the appearance of one image to another. We show that by carefully manipulating the scales of a pyramid decomposition of an image, we can match contrast, texture, noise, and blur, while avoiding image artifacts. The output composite can then be reconstructed from the modified pyramid coefficients while enforcing both alpha-based and seamless boundary constraints. We show how the proposed framework can be used to produce realistic composites with minimal user interaction in a number of different scenarios.

(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,

We propose a set of dynamic shading enhancement techniques for improving the perception of details, features, and overall shape characteristics from images created with Reflectance Transformation Imaging (RTI) techniques. Selection of these perceptual enhancement filters can significantly improve the user’s ability to interactively inspect the content of 2D RTI media by zooming, panning, and changing the illumination direction. In particular, we present two groups of strategies for RTI image enhancement based on two main ideas: exploiting the unsharp masking methodology in the RTI-specific context; and locally optimizing the incident light direction for improved RTI image sharpness and illumination of surface features. The Result section will present a number of datasets and compare them with existing techniques. 6

Figure 1: L0 smoothing accomplished by global small-magnitude gradient removal. Our method suppresses low-amplitude details. Meanwhile it globally retains and sharpens salient edges. Even the high-contrast thin edges on the tower are preserved. We present a new image editing method, particularly effective for sharpening major edges by increasing the steepness of transition while eliminating a manageable degree of low-amplitude structures. The seemingly contradictive effect is achieved in an optimization framework making use of L0 gradient minimization, which can globally control how many non-zero gradients are resulted in to approximate prominent structure in a sparsity-control manner. Unlike other edge-preserving smoothing approaches, our method does not depend on local features, but instead globally locates important edges. It, as a fundamental tool, finds many applications and is particularly beneficial to edge extraction, clip-art JPEG artifact removal, and non-photorealistic effect generation.