2022
DOI: 10.1109/tci.2022.3171417
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Constrained Predictive Filters for Single Image Bokeh Rendering

Abstract: Bokeh rendering is a technique used to take pictures with out-of-focus areas to highlight regions of interest. Due to limitations in hardware and shooting condition, rendering a bokeh image from a full-focus image has attracted a lot of interest. In this paper, we model bokeh rendering as the combination of salient region retention and bokeh blurring, and propose a neural network to generate a realistic bokeh image from a single full-focus image through end-to-end training. Specifically, we propose a gate fusi… Show more

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Cited by 19 publications
(2 citation statements)
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“…Recently, learning-based methods exhibited great potential in image reconstruction tasks (Zheng et al 2022;Chen et al 2023;Zhao et al 2021). (Ehret et al 2019) obtained the processed demosaicing and denoising images by using a convolutional neural network with temporal and spatial redundancy information between consecutively captured images, thereby achieving the joint demosaicing and denoising task in an unsupervised way.…”
Section: Related Workmentioning
confidence: 99%
“…Recently, learning-based methods exhibited great potential in image reconstruction tasks (Zheng et al 2022;Chen et al 2023;Zhao et al 2021). (Ehret et al 2019) obtained the processed demosaicing and denoising images by using a convolutional neural network with temporal and spatial redundancy information between consecutively captured images, thereby achieving the joint demosaicing and denoising task in an unsupervised way.…”
Section: Related Workmentioning
confidence: 99%
“…However, as the L1 loss is a point-wise loss, it does not capture edge information important particularly to minimize ghosting in the HDR reconstruction. Following the novel training strategy of CNN (Zheng et al 2020(Zheng et al , 2021(Zheng et al , 2022a, we adopt the Advanced Sobel Loss (ASL) and combine it with the L1 loss to formulate the loss function for to enhance the edge information, which can be expressed as:…”
Section: Experiments Training Lossmentioning
confidence: 99%