English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Although depth cameras acquire depth in dynamic scenes, the captured depth images are often noisy and of low resolution. Depth images have the physical nature of being represented by smooth regions and edges in between them, i.e. depth images have high edge sparsity in the gradient domain. In this paper, we propose intensity guided depth upsampling using edge sparsity and super-weighted <inline-formula> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> gradient minimization. First, we get mutual structure between intensity and depth using joint mutual structure filtering. Second, we generate an initial depth image using recursive interpolation. Next, we generate weights for <inline-formula> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> gradient minimization based on gradient and entropy of the intensity image, and upsample the depth image using super-weighted <inline-formula> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> gradient minimization. In super-weighted <inline-formula> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> gradient minimization, we combine two main terms: Hybrid data fidelity and weighted <inline-formula> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> gradient regularization. The hybrid data fidelity term combines both zero-order and first-order data differences to suppress staircase artifacts, while the weighted <inline-formula> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> gradient regularization term preserves depth structures and removes noise. Finally, we further refine the depth image using adaptive fast weighted median filtering. Experiments on Middlebury and real-world scene datasets verify that the proposed method produces edge preserving depth upsampling results and outperforms state-of-the-arts in terms of both visual quality and quantitative measurements.

Intensity Guided Depth Upsampling Using Edge Sparsity and Super-Weighted $L_0$ Gradient Minimization