High dynamic range image forensics using cnn
원문 초록
High dynamic range (HDR) imaging has recently drawn much attention in multimedia community. In this paper, we proposed a HDR image forensics method based on convolutional neural network (CNN).To our best knowledge, this is the first time to apply deep learning method on HDR image forensics. The proposed algorithm uses CNN to distinguish HDR images generated by multiple low dynamic range (LDR) images from that expanded by single LDR image using inverse tone mapping (iTM). To do this, we learn the change of statistical characteristics extracted by the proposed CNN architectures and classify two kinds of HDR images. Comparision results with some traditional statistical characteristics shows efficiency of the proposed method in HDR image source identification.
함께 살펴볼 논문
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Redistributing the Precision and Content in 3D-LUT-based Inverse Tone-mapping for HDR/WCG Display
Generalized Lightness Adaptation with Channel Selective Normalization
Lightweight Improved Residual Network for Efficient Inverse Tone Mapping
RealRep: Generalized SDR-to-HDR Conversion with Style Disentangled Representation Learning
HDRT: A Large-Scale Dataset for Infrared-Guided HDR Imaging
Semantic Aware Diffusion Inverse Tone Mapping
토론
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