Diffractive blur and low light levels are two fundamental challenges in producing high-quality photographs in under-display cameras (UDCs). In this paper, we incorporate phase masks on display panels to tackle both challenges. Our design inserts two phase masks, specifically two microlens arrays, in front of and behind a display panel. The first phase mask concentrates light on the locations where the display is transparent so that more light passes through the display, and the second phase mask reverts the effect of the first phase mask. We further optimize the folding height of each microlens to improve the quality of PSFs and suppress chromatic aberration. We evaluate our design using a physically-accurate simulator based on Fourier optics. The proposed design is able to double the light throughput while improving the invertibility of the PSFs. Lastly, we discuss the effect of our design on the display quality and show that implementation with polarization-dependent phase masks can leave the display quality uncompromised.

We propose an information-theoretic knowledge distillation approach for the compression of generative adversarial networks, which aims to maximize the mutual information between teacher and student networks via a variational optimization based on an energy-based model. Because the direct computation of the mutual information in continuous domains is intractable, our approach alternatively optimizes the student network by maximizing the variational lower bound of the mutual information. To achieve a tight lower bound, we introduce an energy-based model relying on a deep neural network to represent a flexible variational distribution that deals with high-dimensional images and consider spatial dependencies between pixels, effectively. Since the proposed method is a generic optimization algorithm, it can be conveniently incorporated into arbitrary generative adversarial networks and even dense prediction networks, e.g., image enhancement models. We demonstrate that the proposed algorithm achieves outstanding performance in model compression of generative adversarial networks consistently when combined with several existing models.

Under-display camera (UDC) technology is essential for full-screen display in smartphones and is achieved by removing the concept of drilling holes on display. However, this causes inevitable image degradation in the form of spatially variant blur and noise because of the opaque display in front of the camera. To address spatially variant blur and noise in UDC images, we propose a novel controllable image restoration algorithm utilizing pixel-wise UDC-specific kernel representation and a noise estimator. The kernel representation is derived from an elaborate optical model that reflects the effect of both normal and oblique light incidence. Also, noise-adaptive learning is introduced to control noise levels, which can be utilized to provide optimal results depending on the user preferences. The experiments showed that the proposed method achieved superior quantitative performance as well as higher perceptual quality on both a real-world dataset and a monitor-based aligned dataset compared to conventional image restoration algorithms.

Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep CNN as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserve the detail texture of the original images.