6,458 research outputs found
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
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
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