Multiresolution example-based depth image restoration (original) (raw)

Wavelet-based stereo images reconstruction using depth images

2007

ABSTRACT It is believed by many that three-dimensional (3D) television will be the next logical development toward a more natural and vivid home entertaiment experience. While classical 3D approach requires the transmission of two video streams, one for each view, 3D TV systems based on depth image rendering (DIBR) require a single stream of monoscopic images and a second stream of associated images usually termed depth images or depth maps, that contain per-pixel depth information.

INTERPOLATION-RESTORATION METHOD FOR SUPERRESOLUTION (WAVELET SUPERRESOLUTION)

Superresolution produces high-quality, high-resolution images from a set of degraded, low-resolution images where relative frame-to-frame motions provide differ- ent looks at the scene. Superresolution translates data temporal bandwidth into enhanced spatial resolution. If considered together on a reference grid, given low-resolution data are nonuniformly sampled. However, data from each frame are sampled regularly on a rectangular grid. This special type of nonuniform sampling is called interlaced sampling. We propose a new wavelet-based interpolation-restoration algorithm for superresolution. Our efficient wavelet interpolation technique takes advantage of the regularity and structure inherent in interlaced data, thereby significantly reducing the computational burden. We present one- and two-dimensional superresolution experiments to demonstrate the effec- tiveness of our algorithm.

Content adaptive wavelet based method for joint denoising of depth and luminance images

2007

abstract In this paper we present a new method for joint denoising of depth and luminance images produced by time-of-flight camera. Here we assume that the sequence does not contain outlier points which can be present in the depth images. Our method first performs estimation of noise and signal covariance matrices and then performs vector denoising. Luminance image is segmented into similar contexts using k-means algorithm, which are used for calculation of covariance matrices.

UPSAMPLING AND DENOISING OF DEPTH MAPS VIA JOINT-SEGMENTATION

2012

ABSTRACT The recent development of low-cost and fast time-of-flight cameras enabled measuring depth information at video frame rates. Although these cameras provide invaluable information for many 3D applications, their imaging capabilities are very limited both in terms of resolution and noise level. In this paper, we present a novel method for obtaining a high resolution depth map from a pair of a low resolution depth map and a corresponding high resolution color image.

IJERT-Patch Based Super Resolution of Depth Maps

International Journal of Engineering Research and Technology (IJERT)`, 2015

https://www.ijert.org/patch-based-super-resolution-of-depth-maps https://www.ijert.org/research/patch-based-super-resolution-of-depth-maps-IJERTV4IS060505.pdf An issue of image technology is to improve the quality of an image. For improving the quality, Super Resolution technique is introduced. Super Resolution refers to the estimation of a high resolution (HR) image from one or more low resolution(LR)images.SR techniques handle the issues of alias removal, deblurring and denoising while interpolating the low resolution inputs. In 3D computer graphics, a depth map is an image or image channel that contains information relating to the distance of the surfaces of scene objects from a view point. Depth Maps have an advantage of limited bandwidth increase and offers flexibility and compatibility than color images. The depth map can vary depending on the depth sensors. One of the most popular depth sensors is Time of Flight (ToF) sensor. They are cheaper but provide depth maps of low resolution. So a patch based super resolution technique for improving the resolution of depth maps is proposed. Experimental results show better values for Peak Signal-to Noise Ratio (PSNR), StructuralSimilarityIndex Measurement (SSIM), Visual Information Fidelity (VIF) and entropy.