Our Tonemapped HDR Image Sen (46.12 dB) Kang (40.02 dB) Ours (48.88 dB) Ground Truth Fig. 1 . We propose a learning-based approach to produce a high-quality HDR image (shown in middle) given three differently exposed LDR images of a dynamic scene (shown on the left). We first use the optical flow method of Liu [2009] to align the images with low and high exposures to the one with medium exposure, which we call the reference image (shown with blue border). Note that, we use reference to refer to the LDR image with the medium exposure, which is different from the ground truth HDR image. Our learning system generates an HDR image, which is aligned to the reference image, but contains information from the other two images. For example, the details on the table are saturated in the reference image, but are visible in the image with the shorter exposure. The method of Kang et al. [2003] is able to recover the saturated regions, but contains some minor artifacts. However, the patch-based method of Sen et al. [2012] is not able to properly reproduce the details in this region because of extreme motion. Moreover, Kang et al. 's method introduces alignment artifacts which appear as tearing in the bottom inset. The method of Sen et al. produces a reasonable result in this region, but their result is noisy since they heavily rely on the reference image. Our method produces a high-quality result, better than other approaches both visually and numerically. See Sec. 4 for details about the process of obtaining the input LDR and ground truth HDR images. The full images as well as comparison against a few other approaches are shown in the supplementary materials. The differences in the results presented throughout the paper are best seen by zooming into the electronic version. Producing a high dynamic range (HDR) image from a set of images with different exposures is a challenging process for dynamic scenes. A category of existing techniques first register the input images to a reference image and then merge the aligned images into an HDR image. However, the artifacts of the registration usually appear as ghosting and tearing in the final HDR images. In this paper, we propose a learning-based approach to address this problem for dynamic scenes. We use a convolutional neural network (CNN) as our learning model and present and compare three different system architectures to model the HDR merge process. Furthermore, we create a large dataset of input LDR images and their corresponding ground truth HDR images to train our system. We demonstrate the performance of our system by producing high-quality HDR images from a set of three LDR images. Experimental results show that our method consistently produces better results than several state-of-the-art approaches on challenging scenes.
Kalantari and Ramamoorthi REF generated HDR images from three unfixed LDR images with different exposure values.
Deep high dynamic range imaging of dynamic scenes
TOGS
Computer Science