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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The intrinsic image decomposition aims to retrieve “intrinsic” properties of an image, such as shading and reflectance. To make it possible to quantitatively compare different approaches to this problem in realistic settings, we present a ground-truth dataset of intrinsic image decompositions for a variety of real-world objects. For each object, we separate an image of it into three components: Lambertian shading, reflectance, and specularities. We use our dataset to quantitatively compare several existing algorithms; we hope that this dataset will serve as a means for evaluating future work on intrinsic images. Abstract. Shadow detection and removal in real scene images is always a challenging but yet intriguing problem. In contrast with the rapidly expanding and continuous interests on this area, the authors are unaware of any comprehensive surveys on this topic. This paper aimed to give a comprehensive and critical survey of current shadow detection and removal methods. Algorithms are categorized into there sets by their different functions and assumptions about the scenes. A discussion of reasonable evaluation is given at the end of this survey. 1 Introduction Shadows and shadings in images have long been disruptive to computer vision algorithms. They appear as surface features, when in fact they are caused by the interaction between light and objects. This may lead to problems in scene understanding, object segmentation, tracking, recognition, etc. Because of the undesirable effects of shadows on image analysis, much attention was paid to the area of shadow detection and removal over the past decades and covered many specific applications such as traffic surveillance [1, 2], face recognition [3, 4, 5] and image segmentation [6]. In spite of these extensive studies, more researches focus on providing a general method for arbitrary scene images and thereby obtaining “visually pleasing” shadow free images. In contrast with the rapidly expanding interests on shadow removal, no comprehensive survey is reported on this particular topic. A list of recent work on this area is reported in [7] but algorithm details are missed. A survey was conducted by A. Prati [8, 9] on the moving cast shadow detection, which is the part of current interests. This paper aims to give a relatively comprehensive study on the current methods of detecting and removing shadows in both still and moving images. Before going into the detailed algorithms, we first review some different kinds of shadows in natural scenes. We suggest that an appropriate role of early visual processing is to describe a scene in terms of intrinsic (vertical) characteristics -- such as range, orientation, reflectance, and incident illumination -- of the surface element visible at each point in the image. Support for this idea comes from three sources: the obvious utility of intrinsic characteristics for higher-level scene analysis; the apparent ability of humans to determine these characteristics, regardless of viewing conditions or familiarity with the scene; and a theoretical argument that such a description is obtainable, by a noncognitive and nonpurposive process, at least, for simple scene domains. The central problem in recovering intrinsic scene characteristics is that the information is confounded in the original light-intensity image: a single intensity value encodes all the characteristics of the corresponding scene point. Recovery depends on exploiting constraints, derived from assumptions about the nature of the scene and the physics of the imaging process. This paper presents a survey and a comparative evaluation of recent techniques for moving cast shadow detection. We identify shadow removal as a critical step for improving object detection and tracking. The survey covers methods published during the last decade, and places them in a feature-based taxonomy comprised of four categories: chromacity, physical, geometry and textures. A selection of prominent methods across the categories is compared in terms of quantitative performance measures (shadow detection and discrimination rates, colour desaturation) as well as qualitative observations. Furthermore, we propose the use of tracking performance as an unbiased approach for determining the practical usefulness of shadow detection methods. The evaluation indicates that all shadow detection approaches make different contributions and all have individual strength and weaknesses. Out of the selected methods, the geometry-based technique has strict assumptions and is not generalisable to various environments, but it is a straightforward choice when the objects of interest are easy to model and their shadows have different orientation. The chromacity based method is the fastest to implement and run, but it is sensitive to noise and less effective in low saturated scenes. The physical method improves upon the accuracy of the chromacity method by adapting to local shadow models, but fails when the spectral properties of the objects are similar to that of the background. The small-region texture based method is especially robust for pixels whose neighbourhood is textured, but may take longer to implement and is the most computationally expensive. The large-region texture based method produces the most accurate results, but has a significant computational load due to its multiple processing steps.