Continuous wavelet transform of multiview images using wavelets based on voxel patterns
We propose the multiview wavelets based on voxel patterns of autostereoscopic multiview displays. Direct and inverse continuous wavelet transforms of binary and gray-scale images were performed. The input to the inverse wavelet transform was the array of wavelet coefficients of the direct transform. A restored image reproduces the structure of the multiview image correctly. Also, we modified the dimension of the parallax and the depth of 3D images. The restored and modified images were displayed in 3D using lenticular plates. In each case, the visual 3D picture corresponds to the applied modifications. The results can be applied to the autostereoscopic 3D displays.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Wavelets and continuous wavelet transform for autostereoscopic multiview images
Recently, the reference functions for the synthesis and analysis of the autostereoscopic multiview and integral images in three-dimensional displays we introduced. In the current paper, we propose the wavelets to analyze…
Scale-covariant spiking wavelets
We establish a theoretical connection between wavelet transforms and spiking neural networks through scale-space theory. We rely on the scale-covariant guarantees in the leaky integrate-and-fire neurons to implement disc…
GHM Wavelet Transform for Deep Image Super Resolution
The GHM multi-level discrete wavelet transform is proposed as preprocessing for image super resolution with convolutional neural networks. Previous works perform analysis with the Haar wavelet only. In this work, 37 sing…
Image Super-ResolutionSuper-Resolution2D Empirical Transforms. Wavelets, Ridgelets and Curvelets revisited
A recently developed new approach, called ``Empirical Wavelet Transform'', aims to build 1D adaptive wavelet frames accordingly to the analyzed signal. In this paper, we present several extensions of this approach to 2D …
Encoding and Decoding Temporal Signals with Spiking Bandpass Wavelets
Spike-based encodings are sparse and energy-efficient, but have largely been formulated probabilistically, disconnected from most signal processing literature. We recast spike encoders as time-causal wavelet frames with …