2,281 research outputs found
End-to-End Differentiable Learning to HDR Image Synthesis for Multi-exposure Images
Recently, high dynamic range (HDR) image reconstruction based on the multiple
exposure stack from a given single exposure utilizes a deep learning framework
to generate high-quality HDR images. These conventional networks focus on the
exposure transfer task to reconstruct the multi-exposure stack. Therefore, they
often fail to fuse the multi-exposure stack into a perceptually pleasant HDR
image as the inversion artifacts occur. We tackle the problem in stack
reconstruction-based methods by proposing a novel framework with a fully
differentiable high dynamic range imaging (HDRI) process. By explicitly using
the loss, which compares the network's output with the ground truth HDR image,
our framework enables a neural network that generates the multiple exposure
stack for HDRI to train stably. In other words, our differentiable HDR
synthesis layer helps the deep neural network to train to create multi-exposure
stacks while reflecting the precise correlations between multi-exposure images
in the HDRI process. In addition, our network uses the image decomposition and
the recursive process to facilitate the exposure transfer task and to
adaptively respond to recursion frequency. The experimental results show that
the proposed network outperforms the state-of-the-art quantitative and
qualitative results in terms of both the exposure transfer tasks and the whole
HDRI process
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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