SPECULAR LIGHT PHOTOGRAPHY FULL
A handheld LF camera mounts an array of microlens in front of the sensor, which could record the full 4D rays to describe the scene, so one can refocus the image after a passive single–shot capture and shift viewpoints within sub-apertures of the main lens. īenefiting from computational photography and light field (LF) imaging technologies, we propose an accurate and novel framework that uses LF cameras to remove specularity. They achieve acceptable results in some images, but they are unstable when the analyzed image has complicated textures and extreme specularities. Single-image based approaches require color or texture analysis. Nevertheless, obtaining such an image sequence is difficult, time-consuming or even impractical. Multiple-image based approaches involve an image sequence of the same scene taken either from different viewpoints, with different illumination or utilizing an additional polarizing filter. Based on the number of input images, these methods could be divided into two main categories: multiple-image based and single-image based. In recent years, various techniques try to handle the problem of specular reflections. As a result, processing images with specular reflections using these algorithms can lead to significant inaccuracies. However, a vast majority of materials contain both diffuse and specular reflections in the real world. Most algorithms in computer vision such as segmentation (which typically assumes the intensity changes uniformly or smoothly across a surface), or stereo matching, recognition, image analysis and tracking (they attempt to match images taken from various conditions, i.e., viewing angle, illumination or distance, so they need a consistent surface of an object in different images) ignore the presence of specular pixels and work under the assumption of perfect diffuse surfaces.
They appear as surface features, but in fact they are artifacts caused by illumination changes from different viewing angles. Image specular reflection has long been problematic in computer vision tasks.
0 kommentar(er)
