Unsupervised HDR Imaging: What Can Be Learned from a Single 8-bit Video?
원문 초록
Recently, Deep Learning-based methods for inverse tone-mapping standard dynamic range (SDR) images to obtain high dynamic range (HDR) images have become very popular. These methods manage to fill over-exposed areas convincingly both in terms of details and dynamic range. Typically, these methods, to be effective, need to learn from large datasets and to transfer this knowledge to the network weights. In this work, we tackle this problem from a completely different perspective. What can we learn from a single SDR video? With the presented zero-shot approach, we show that, in many cases, a single SDR video is sufficient to be able to generate an HDR video of the same quality or better than other state-of-the-art methods.
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
Distilling Style from Image Pairs for Global Forward and Inverse Tone Mapping
Text2Light: Zero-Shot Text-Driven HDR Panorama Generation
Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New Benchmark
토론
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