Shadows often create unwanted artifacts in photographs, and removing them can be very challenging. Previous shadow removal methods often produce de-shadowed regions that are visually inconsistent with the rest of the image. In this work we propose a fully automatic shadow region harmonization approach that improves the appearance compatibility of the de-shadowed region as typically produced by previous methods. It is based on a shadow-guided patch-based image synthesis approach that reconstructs the shadow region using patches sampled from non-shadowed regions. The result is then refined based on the reconstruction confidence to handle unique image patterns. Many shadow removal results and comparisons are show the effectiveness of our improvement. Quantitative evaluation on a benchmark dataset suggests that our automatic shadow harmonization approach effectively improves upon the state-of-the-art.
is an extensively studied problem and modern approaches are well summarized in recent surveys @cite_21 @cite_6 . Shadow analysis is also closely related to intrinsic image decomposition @cite_4 @cite_19 -- the problem of separating an image into reflectance and illumination components -- though shadow removal focuses on the illumination variation caused by occluded light sources.
@cite_19 @cite_21 @cite_4 @cite_6
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We propose a data-driven approach for intrinsic image decomposition, which is the process of inferring the confounding factors of reflectance and shading in an image. We pose this as a two-stage learning problem. First, we train a model to predict relative reflectance ordering between image patches ('brighter', 'darker', 'same') from large-scale human annotations, producing a data-driven reflectance prior. Second, we show how to naturally integrate this learned prior into existing energy minimization frame-works for intrinsic image decomposition. We compare our method to the state-of-the-art approach of [7] on both decomposition and image relighting tasks, demonstrating the benefits of the simple relative reflectance prior, especially for scenes under challenging lighting conditions. We present a method for intrinsic image decomposition, which aims to decompose images into reflectance and shading layers. Our input is a sequence of images with varying illumination acquired by a static camera, e.g. an indoor scene with a moving light source or an outdoor timelapse. We leverage the local color variations observed over time to infer constraints on the reflectance and solve the ill-posed image decomposition problem. In particular, we derive an adaptive local energy from the observations of each local neighborhood over time, and integrate distant pairwise constraints to enforce coherent decomposition across all surfaces with consistent shading changes. Our method is solely based on multiple observations of a Lambertian scene under varying illumination and does not require user interaction, scene geometry, or an explicit lighting model. We compare our results with several intrinsic decomposition methods on a number of synthetic and captured datasets. In this paper, we address the problem of shadow detection and removal from single images of natural scenes. Different from traditional methods that explore pixel or edge information, we employ a region based approach. In addition to considering individual regions separately, we predict relative illumination conditions between segmented regions from their appearances and perform pairwise classification based on such information. Classification results are used to build a graph of segments, and graph-cut is used to solve the labeling of shadow and non-shadow regions. Detection results are later refined by image matting, and the shadow free image is recovered by relighting each pixel based on our lighting model. We evaluate our method on the shadow detection dataset in [19]. In addition, we created a new dataset with shadow-free ground truth images, which provides a quantitative basis for evaluating shadow removal. In this paper, we address the problem of shadow detection and removal from single images of natural scenes. Differently from traditional methods that explore pixel or edge information, we employ a region-based approach. In addition to considering individual regions separately, we predict relative illumination conditions between segmented regions from their appearances and perform pairwise classification based on such information. Classification results are used to build a graph of segments, and graph-cut is used to solve the labeling of shadow and nonshadow regions. Detection results are later refined by image matting, and the shadow-free image is recovered by relighting each pixel based on our lighting model. We evaluate our method on the shadow detection dataset in . In addition, we created a new dataset with shadow-free ground truth images, which provides a quantitative basis for evaluating shadow removal. We study the effectiveness of features for both unary and pairwise classification.