Unsupervised HDR Imaging: What Can Be Learned from a Single 8-bit Video?
This work addresses the need for efficient HDR imaging without large datasets, offering a practical solution for video enhancement applications.
The paper tackles the problem of generating high dynamic range (HDR) videos from standard dynamic range (SDR) inputs by proposing an unsupervised, zero-shot approach that learns from a single 8-bit video, achieving quality comparable to or better than state-of-the-art methods.
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.