Low-light imaging with handheld mobile devices is a challenging issue. Limited by the existing models and training data, most existing methods cannot be effectively applied in real scenarios. In this paper, we propose a new low-light image restoration method by using the complementary information of short- and long-exposure images. We first propose a novel data generation method to synthesize realistic short- and long-exposure raw images by simulating the imaging pipeline in low-light environment. Then, we design a new long-short-exposure fusion network (LSFNet) to deal with the problems of low-light image fusion, including high noise, motion blur, color distortion and misalignment. The proposed LSFNet takes pairs of short- and long-exposure raw images as input, and outputs a clear RGB image. Using our data generation method and the proposed LSFNet, we can recover the details and color of the original scene, and improve the low-light image quality effectively. Experiments demonstrate that our method can outperform the state-of-the-art methods.

Correcting the optical aberrations and the manufacturing deviations of cameras is a challenging task. Due to the limitation on volume and the demand for mass production, existing mobile terminals cannot rectify optical degradation. In this work, we systematically construct the perturbed lens system model to illustrate the relationship between the deviated system parameters and the spatial frequency response (SFR) measured from photographs. To further address this issue, an optimization framework is proposed based on this model to build proxy cameras from the machining samples' SFRs. Engaging with the proxy cameras, we synthetic data pairs, which encode the optical aberrations and the random manufacturing biases, for training the learning-based algorithms. In correcting aberration, although promising results have been shown recently with convolutional neural networks, they are hard to generalize to stochastic machining biases. Therefore, we propose a dilated Omni-dimensional dynamic convolution (DOConv) and implement it in post-processing to account for the manufacturing degradation. Extensive experiments which evaluate multiple samples of two representative devices demonstrate that the proposed optimization framework accurately constructs the proxy camera. And the dynamic processing model is well-adapted to manufacturing deviations of different cameras, realizing perfect computational photography. The evaluation shows that the proposed method bridges the gap between optical design, system machining, and post-processing pipeline, shedding light on the joint of image signal reception (lens and sensor) and image signal processing (ISP).

Due to the limitation of hardware, infrared (IR) images have low-resolution (LR) and poor visual quality. Image super-resolution (SR) is a good solution to this problem. In this paper, we present a new convolution network (CNN) to improve the spatial resolution of infrared (IR) images. Our network is able to restore fine details by decomposing the input image into low-frequency and high-frequency domains. In low-frequency domains, we reconstruct image structure by deep networks. In high frequency domains, we reconstruct IR image details. Furthermore, we proposed another network to remove artifacts. Additionally, we propose a new loss function using visible (VIS) images to enhance the details of IR images. In training phase, we use VIS images to guide IR image restoration and in testing phase we get SR IR images with LR IR images input only. We optimize our deep network with a targeted function which penalizes images at different semantic levels using the corresponding terms. Besides, we build a dataset where paired LR-VIS images on the same scene are captured by a camera with both infrared and visible light sensors which both sensors have the same optical axis. Extensive experiments demonstrate that the proposed algorithm achieves superior performance and visual improvements against the state-of-the-arts.