Reflection Decomposition In Single Images Using An Optimum Thresholding-based Method

Traditional methods of separating reflection components have been developed based on multiple images. There are only few methods which are able to use a single image. However, their applicability is limited due to offline setting of its arbitrary parameter. In this study, we propose an effective method to separate specular components using a single image which based on an optimum thresholding-based technique. This method employs modified specular-free image and selects an optimum value for the offset parameter. In contrast to prior method, the proposed method processes all the steps automatically and produces better performance. Experimental results for inhomogeneous objects demonstrate the promising applicability for real-time implementation. However, this method is unsuitable for objects with strong specular reflection. An extension is suggested to include the specular lobe reflectance into Shafer dichromatic model

Embedded polarizing filters to separate diffuse and specular reflection

Polarizing filters provide a powerful way to separate diffuse and specular reflection; however, traditional methods rely on several captures and require proper alignment of the filters. Recently, camera manufacturers have proposed to embed polarizing micro-filters in front of the sensor, creating a mosaic of pixels with different polarizations. In this paper, we investigate the advantages of such camera designs. In particular, we consider different design patterns for the filter arrays and propose an algorithm to demosaic an image generated by such cameras. This essentially allows us to separate the diffuse and specular components using a single image. The performance of our algorithm is compared with a color-based method using synthetic and real data. Finally, we demonstrate how we can recover the normals of a scene using the diffuse images estimated by our method.Comment: ACCV 201

Depth Estimation for Glossy Surfaces with Light-Field Cameras

Abstract. Light-field cameras have now become available in both consumer and industrial applications, and recent papers have demonstrated practical algorithms for depth recovery from a passive single-shot capture. However, current light-field depth estimation methods are designed for Lambertian objects and fail or degrade for glossy or specular surfaces. Because light-field cameras have an array of micro-lenses, the captured data allows modification of both focus and perspec-tive viewpoints. In this paper, we develop an iterative approach to use the benefits of light-field data to estimate and remove the specular component, improving the depth estimation. The approach enables light-field data depth estimation to sup-port both specular and diffuse scenes. We present a physically-based method that estimates one or multiple light source colors. We show our method outperforms current state-of-the-art diffuse and specular separation and depth estimation al-gorithms in multiple real world scenarios.

Recaptured photo detection using specularity distribution

Detection of planar surfaces in a generic scene is difficult when the illumination is complex and less intense, and the surfaces have non-uniform colors (e.g., a movie poster). As a result, the specularity, if appears, is superimposed with the surface color pattern, and hence the observation of uniform specularity is no longer sufficient for identifying planar sur-faces in a generic scene as it does under a distant point light source. In this paper, we address the problem of planar sur-face recognition in a single generic-scene image. In partic-ular, we study the problem of recaptured photo recognition as an application in image forensics. We discover that the specularity of a recaptured photo is modulated by the micro-structure of the photo surface, and its spatial distribution can be used for differentiating recaptured photos from the origi-nal photos. We validate our findings in real images of generic scenes. Experimental results show that there is a distinguish-able feature of natural scene and recaptured images. Given the definition of specular ratio as the percentage of specularity in the overall measured intensity, the distribution of specular ra-tio image’s gradient of natural images is Laplacian-like while that of recaptured images is Rayleigh-like. Index Terms — Image forensics, recaptured photo detec-tion, dichromatic reflectance model, specularity 1