262 research outputs found
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers
In image restoration tasks, like denoising and super resolution, continual
modulation of restoration levels is of great importance for real-world
applications, but has failed most of existing deep learning based image
restoration methods. Learning from discrete and fixed restoration levels, deep
models cannot be easily generalized to data of continuous and unseen levels.
This topic is rarely touched in literature, due to the difficulty of modulating
well-trained models with certain hyper-parameters. We make a step forward by
proposing a unified CNN framework that consists of few additional parameters
than a single-level model yet could handle arbitrary restoration levels between
a start and an end level. The additional module, namely AdaFM layer, performs
channel-wise feature modification, and can adapt a model to another restoration
level with high accuracy. By simply tweaking an interpolation coefficient, the
intermediate model - AdaFM-Net could generate smooth and continuous restoration
effects without artifacts. Extensive experiments on three image restoration
tasks demonstrate the effectiveness of both model training and modulation
testing. Besides, we carefully investigate the properties of AdaFM layers,
providing a detailed guidance on the usage of the proposed method.Comment: Accepted by CVPR 2019 (oral); code is available:
https://github.com/hejingwenhejingwen/AdaF
Plasmonic Nanoantenna Array Design
Recently, wireless optical communication system is developing toward the chip level. Optical nanoantenna array in optical communication system is the key component for radiating and receiving light. In this chapter, we propose a sub-wavelength plasmonic nanoantenna with high gain operating at the standard optical communication wavelength of 1550Â nm. The designed plasmonic antenna has a good matching with the silicon waveguide in a wide band, and light is fed from the bottom of the nanoantenna via the silicon waveguide. Furthermore, we design two kinds of antenna arrays with the proposed plasmonic nanoantenna, including one- and two-dimensional arrays (1Â Ă—Â 8 and 8Â Ă—Â 8). The radiation characteristics of the antenna arrays are investigated and both arrays have high gains and wide beam steering range without grating lobes
Optimization of “Deoxidation Alloying” Batching Scheme
In this paper, a mathematical model was established to predict the deoxidation alloying and to optimize the type and quantity of input alloys. Firstly, the GCA method was used to obtain the main factors affecting the alloy yield of carbon and manganese based on the historical data. Secondly, the alloy yield was predicted by the stepwise MRA, the BP neural network and the regression SVM models, respectively. The conclusion is that the regression SVM model has the highest prediction accuracy and the maximum deviation between the test set prediction result and the real value was only 0.0682 and 0.0554. Thirdly, in order to reduce the manufacturer's production cost, the genetic algorithm was used to calculate the production cost mathematical programming model. Finally, sensitivity analysis was performed on the prediction model and the cost optimization model. The unit price of 20% of the alloy raw materials was increased by 20%, and the total cost change rate was 0.7155%, the lowest was -0.4297%, which proved that the mathematical model established presented strong robustness and could be certain reference value for the current production of iron and steel enterprises
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
We present DiffBIR, which leverages pretrained text-to-image diffusion models
for blind image restoration problem. Our framework adopts a two-stage pipeline.
In the first stage, we pretrain a restoration module across diversified
degradations to improve generalization capability in real-world scenarios. The
second stage leverages the generative ability of latent diffusion models, to
achieve realistic image restoration. Specifically, we introduce an injective
modulation sub-network -- LAControlNet for finetuning, while the pre-trained
Stable Diffusion is to maintain its generative ability. Finally, we introduce a
controllable module that allows users to balance quality and fidelity by
introducing the latent image guidance in the denoising process during
inference. Extensive experiments have demonstrated its superiority over
state-of-the-art approaches for both blind image super-resolution and blind
face restoration tasks on synthetic and real-world datasets. The code is
available at https://github.com/XPixelGroup/DiffBIR
Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation
Modern displays are capable of rendering video content with high dynamic
range (HDR) and wide color gamut (WCG). However, the majority of available
resources are still in standard dynamic range (SDR). As a result, there is
significant value in transforming existing SDR content into the HDRTV standard.
In this paper, we define and analyze the SDRTV-to-HDRTV task by modeling the
formation of SDRTV/HDRTV content. Our analysis and observations indicate that a
naive end-to-end supervised training pipeline suffers from severe gamut
transition errors. To address this issue, we propose a novel three-step
solution pipeline called HDRTVNet++, which includes adaptive global color
mapping, local enhancement, and highlight refinement. The adaptive global color
mapping step uses global statistics as guidance to perform image-adaptive color
mapping. A local enhancement network is then deployed to enhance local details.
Finally, we combine the two sub-networks above as a generator and achieve
highlight consistency through GAN-based joint training. Our method is primarily
designed for ultra-high-definition TV content and is therefore effective and
lightweight for processing 4K resolution images. We also construct a dataset
using HDR videos in the HDR10 standard, named HDRTV1K that contains 1235 and
117 training images and 117 testing images, all in 4K resolution. Besides, we
select five metrics to evaluate the results of SDRTV-to-HDRTV algorithms. Our
final results demonstrate state-of-the-art performance both quantitatively and
visually. The code, model and dataset are available at
https://github.com/xiaom233/HDRTVNet-plus.Comment: Extended version of HDRTVNe
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