Projection defocus correction using adaptive kernel sampling and geometric correction in dual-planar environments

Ladha, Shamsuddin; Smith‐Miles, Kate; Chandran, Sharat

doi:10.1109/cvprw.2011.5981686

Cited by 4 publications

(1 citation statement)

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“…Given RGB image patches and the corresponding depth and location maps as input, our model extracts highdimensional feature maps and performs non-linear mapping operation to predict the defocused version. Since optical blurring effects are color-channel dependent [8,15,27,28], the MC-RDN model deploys three individual convolutional layers to extract the low-level features in the Red (R), Green (G), and Blue (B) channels of the input images as…”

Section: Network Architecturementioning

confidence: 99%

Multi-channel residual network model for accurate estimation of spatially-varying and depth-dependent defocus kernels

Cao

et al. 2020

Opt. Express

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Digital projectors have been increasingly utilized in various commercial and scientific applications. However, they are prone to the out-of-focus blurring problem since their depthof-fields are typically limited. In this paper, we explore the feasibility of utilizing a deep learning-based approach to analyze the spatially-varying and depth-dependent defocus properties of digital projectors. A multimodal displaying/imaging system is built for capturing images projected at various depths. Based on the constructed dataset containing well-aligned in-focus, out-of-focus, and depth images, we propose a novel multi-channel residual deep network model to learn the end-to-end mapping function between the in-focus and out-of-focus image patches captured at different spatial locations and depths. To the best of our knowledge, it is the first research work revealing that the complex spatially-varying and depth-dependent blurring effects can be accurately learned from a number of real-captured image pairs instead of being hand-crafted as before. Experimental results demonstrate that our proposed deep learning-based method significantly outperforms the state-of-the-art defocus kernel estimation techniques and thus leads to better out-of-focus compensation for extending the dynamic ranges of digital projectors.

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Section: Network Architecturementioning

confidence: 99%