We propose an end-to-end trainable Convolutional Neural Network (CNN), named GridDehazeNet, for single image dehazing. The GridDehazeNet consists of three modules: pre-processing, backbone, and post-processing. The trainable pre-processing module can generate learned inputs with better diversity and more pertinent features as compared to those derived inputs produced by handselected pre-processing methods. The backbone module implements a novel attention-based multi-scale estimation on a grid network, which can effectively alleviate the bottleneck issue often encountered in the conventional multi-scale approach. The post-processing module helps to reduce the artifacts in the final output. Experimental results indicate that the GridDehazeNet outperforms the state-of-the-arts on both synthetic and real-world images. The proposed hazing method does not rely on the atmosphere scattering model, and we provide an explanation as to why it is not necessarily beneficial to take advantage of the dimension reduction offered by the atmosphere scattering model for image dehazing, even if only the dehazing results on synthetic images are concerned. Project website: https: //proteus1991.github.io/GridDehazeNet/.

Figure 1. Existing super-resolution method (RDN [38]) does not perform well for real captured images as shown in (c). We can obtain sharper results (d) by re-training existing model [38] with the data generated by our method. Furthermore, we recover more structures and details (e) by exploiting the radiance information recorded in raw images. The two input images (a) are captured by Leica SL Typ-601 and iPhone 6s respectively, and both cameras are not seen by the models during training. Results best viewed electronically with zoom. AbstractMost existing super-resolution methods do not perform well in real scenarios due to lack of realistic training data and information loss of the model input. To solve the first problem, we propose a new pipeline to generate realistic training data by simulating the imaging process of digital cameras. And to remedy the information loss of the input, we develop a dual convolutional neural network to exploit the originally captured radiance information in raw images. In addition, we propose to learn a spatially-variant color transformation which helps more effective color corrections. Extensive experiments demonstrate that superresolution with raw data helps recover fine details and clear structures, and more importantly, the proposed network and data generation pipeline achieve superior results for single image super-resolution in real scenarios.

Super-resolution is a fundamental problem in computer vision which aims to overcome the spatial limitation of camera sensors. While significant progress has been made in single image super-resolution, most algorithms only perform well on synthetic data, which limits their applications in real scenarios. In this paper, we study the problem of real-scene single image super-resolution to bridge the gap between synthetic data and real captured images. We focus on two issues of existing super-resolution algorithms: lack of realistic training data and insufficient utilization of visual information obtained from cameras. To address the first issue, we propose a method to generate more realistic training data by mimicking the imaging process of digital cameras. For the second issue, we develop a two-branch convolutional neural network to exploit the radiance information originally-recorded in raw images. In addition, we propose a dense channel-attention block for better image restoration as well as a learning-based guided filter network for effective color correction. Our model is able to generalize to different cameras without deliberately training on images from specific camera types. Extensive experiments demonstrate that the proposed algorithm can recover fine details and clear structures, and achieve high-quality results for single image super-resolution in real scenes.

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We examine the profile of second harmonic generation (SHG) for GaAs/GaAlAs spherical quantum dots (QDs) of Woods-Saxon (WS) plus attractive inversely quadratic (AIQ) potential under the joint influence of additional factors (pressure and temperature) and structural parameters (strengths and radius). The energies and wave functions in GaAs/GaAlAs spherical QDs under WS-AIQ limiting potential are calculated using parametric Nikiforov-Uvarov (NU) method. Depending on the calculated energies and corresponding wave functions, the SHG coefficient is examined by the iterative procedure in the density matrix method for this system. Finally, the calculated results display that a strong SHG coefficient response, and red shift or blue shift energy can be acquired by adjusting parameters.