286 research outputs found
FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network
High dynamic range (HDR) image generation from a single exposure low dynamic
range (LDR) image has been made possible due to the recent advances in Deep
Learning. Various feed-forward Convolutional Neural Networks (CNNs) have been
proposed for learning LDR to HDR representations. To better utilize the power
of CNNs, we exploit the idea of feedback, where the initial low level features
are guided by the high level features using a hidden state of a Recurrent
Neural Network. Unlike a single forward pass in a conventional feed-forward
network, the reconstruction from LDR to HDR in a feedback network is learned
over multiple iterations. This enables us to create a coarse-to-fine
representation, leading to an improved reconstruction at every iteration.
Various advantages over standard feed-forward networks include early
reconstruction ability and better reconstruction quality with fewer network
parameters. We design a dense feedback block and propose an end-to-end feedback
network- FHDR for HDR image generation from a single exposure LDR image.
Qualitative and quantitative evaluations show the superiority of our approach
over the state-of-the-art methods.Comment: 2019 IEEE Global Conference on Signal and Information Processing
(GlobalSIP
Fully-automatic inverse tone mapping algorithm based on dynamic mid-level tone mapping
High Dynamic Range (HDR) displays can show images with higher color contrast levels and peak luminosities than the common Low Dynamic Range (LDR) displays. However, most existing video content is recorded and/or graded in LDR format. To show LDR content on HDR displays, it needs to be up-scaled using a so-called inverse tone mapping algorithm. Several techniques for inverse tone mapping have been proposed in the last years, going from simple approaches based on global and local operators to more advanced algorithms such as neural networks. Some of the drawbacks of existing techniques for inverse tone mapping are the need for human intervention, the high computation time for more advanced algorithms, limited low peak brightness, and the lack of the preservation of the artistic intentions. In this paper, we propose a fully-automatic inverse tone mapping operator based on mid-level mapping capable of real-time video processing. Our proposed algorithm allows expanding LDR images into HDR images with peak brightness over 1000 nits, preserving the artistic intentions inherent to the HDR domain. We assessed our results using the full-reference objective quality metrics HDR-VDP-2.2 and DRIM, and carrying out a subjective pair-wise comparison experiment. We compared our results with those obtained with the most recent methods found in the literature. Experimental results demonstrate that our proposed method outperforms the current state-of-the-art of simple inverse tone mapping methods and its performance is similar to other more complex and time-consuming advanced techniques
DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs
We present a novel deep learning architecture for fusing static
multi-exposure images. Current multi-exposure fusion (MEF) approaches use
hand-crafted features to fuse input sequence. However, the weak hand-crafted
representations are not robust to varying input conditions. Moreover, they
perform poorly for extreme exposure image pairs. Thus, it is highly desirable
to have a method that is robust to varying input conditions and capable of
handling extreme exposure without artifacts. Deep representations have known to
be robust to input conditions and have shown phenomenal performance in a
supervised setting. However, the stumbling block in using deep learning for MEF
was the lack of sufficient training data and an oracle to provide the
ground-truth for supervision. To address the above issues, we have gathered a
large dataset of multi-exposure image stacks for training and to circumvent the
need for ground truth images, we propose an unsupervised deep learning
framework for MEF utilizing a no-reference quality metric as loss function. The
proposed approach uses a novel CNN architecture trained to learn the fusion
operation without reference ground truth image. The model fuses a set of common
low level features extracted from each image to generate artifact-free
perceptually pleasing results. We perform extensive quantitative and
qualitative evaluation and show that the proposed technique outperforms
existing state-of-the-art approaches for a variety of natural images.Comment: ICCV 201
Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline
Recovering a high dynamic range (HDR) image from a single low dynamic range
(LDR) input image is challenging due to missing details in under-/over-exposed
regions caused by quantization and saturation of camera sensors. In contrast to
existing learning-based methods, our core idea is to incorporate the domain
knowledge of the LDR image formation pipeline into our model. We model the
HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2)
non-linear mapping from a camera response function, and (3) quantization. We
then propose to learn three specialized CNNs to reverse these steps. By
decomposing the problem into specific sub-tasks, we impose effective physical
constraints to facilitate the training of individual sub-networks. Finally, we
jointly fine-tune the entire model end-to-end to reduce error accumulation.
With extensive quantitative and qualitative experiments on diverse image
datasets, we demonstrate that the proposed method performs favorably against
state-of-the-art single-image HDR reconstruction algorithms.Comment: CVPR 2020. Project page:
https://www.cmlab.csie.ntu.edu.tw/~yulunliu/SingleHDR Code:
https://github.com/alex04072000/SingleHD
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