Penrose high-dynamic-range imaging

Li, Jia; Bai, Chenyan; Lin, Zhouchen; Yu, Jian

doi:10.1117/1.jei.25.3.033024

Cited by 5 publications

(1 citation statement)

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“…While we focus in this paper mainly on the mathematical theory on triangular grids, we mention, for instance, that honeycombed octagonal CCD sensor layouts are in use in consumer cameras, e.g., the Fujifilm SuperCCD sensor. Furthermore, non-rectangular sub-pixel configurations appear to be promising for spatially varying exposure (SVE) sensors for high-dynamic-range (HDR) imaging, see [41], and super-resolution applications, see [8,51,60]. Image processing problems on non-regular pixel layouts have been previously considered in [20,37,55,38].…”

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confidence: 99%

Discrete Total Variation with Finite Elements and Applications to Imaging

et al. 2018

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The total variation (TV)-seminorm is considered for piecewise polynomial, globally discontinuous (DG) and continuous (CG) finite element functions on simplicial meshes. A novel, discrete variant (DTV) based on a nodal quadrature formula is defined. DTV has favorable properties, compared to the original TV-seminorm for finite element functions. These include a convenient dual representation in terms of the supremum over the space of Raviart-Thomas finite element functions, subject to a set of simple constraints. It can therefore be shown that a variety of algorithms for classical image reconstruction problems, including TV-L 2 and TV-L 1 , can be implemented in low and higher-order finite element spaces with the same efficiency as their counterparts originally developed for images on Cartesian grids.1. Introduction. The total-variation (TV)-seminorm | · | T V is ubiquitous as a regularizing functional in image analysis and related applications; see for instance [50,28,17,14]. When Ω ⊂ R 2 is a bounded domain, this seminorm is defined aswhere s ∈ [1, ∞], s * = s s−1 denotes the conjugate of s and | · | s * is the usual s * -norm of vectors in R 2 . Frequent choices include s = 2 (the isotropic case) and s = 1, see Figure 1.1.It has been observed in [21] that "the rigorous definition of the TV for discrete images has received little attention." In this paper we propose and analyze a discrete analogue of (1.1) for functions u belonging to a space DG r (Ω) or CG r (Ω) of globally discontinuous or continuous finite element functions of polynomial degree 1 0 ≤ r ≤ 4 on a geometrically conforming, simplicial triangulation of Ω, consisting of triangles T and interior edges E. 2 * This work was supported by DFG grants HE 6077/10-1 and SCHM 3248/2-1 within the Priority Program SPP 1962 (Non-smooth and Complementarity-based Distributed Parameter Systems: Simulation and Hierarchical Optimization), which is gratefully acknowledged.

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Discrete Total Variation with Finite Elements and Applications to Imaging

et al. 2018

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Single-Shot High Dynamic Range Imaging via Multiscale Convolutional Neural Network

Vien

Lee

2021

IEEE Access

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We propose a single-shot high dynamic range (HDR) imaging algorithm with row-wise varying exposures in a single raw image based on a deep convolutional neural network (CNN). We first convert a raw Bayer input image into a radiance map by calibrating rows with different exposures, and then we design a new CNN model to restore missing information at the under-and over-exposed pixels and reconstruct color information from the raw radiance map. The proposed CNN model consists of three branch networks to obtain multiscale feature maps for an image. To effectively estimate the high-quality HDR images, we develop a robust loss function that considers the human visual system (HVS) model, color perception model, and multiscale contrast. Experimental results on both synthetic and captured real images demonstrate that the proposed algorithm can achieve synthesis results of significantly higher quality than conventional algorithms in terms of structure, color, and visual artifacts.INDEX TERMS Spatially varying exposure (SVE) image, high dynamic range (HDR) imaging, convolutional neural network (CNN), and human visual system (HVS).

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Single-shot high dynamic range imaging via deep convolutional neural network

Lee

2017

2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

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Penrose high-dynamic-range imaging

Cited by 5 publications

References 63 publications

Discrete Total Variation with Finite Elements and Applications to Imaging

Discrete Total Variation with Finite Elements and Applications to Imaging

Single-Shot High Dynamic Range Imaging via Multiscale Convolutional Neural Network

Single-shot high dynamic range imaging via deep convolutional neural network

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