KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image

Low-light images often suffer from noise and color distortion. Object detection, semantic segmentation, instance segmentation, and other tasks are challenging when working with low-light images because of image noise and chromatic aberration. We also found that the conventional Retinex theory loses information in adjusting the image for low-light tasks. In response to the aforementioned problem, this paper proposes an algorithm for low illumination enhancement. The proposed method, KinD-LCE, uses a light curve estimation module to enhance the illumination map in the Retinex decomposed image, improving the overall image brightness. An illumination map and reflection map fusion module were also proposed to restore the image details and reduce detail loss. Additionally, a TV(total variation) loss function was applied to eliminate noise. Our method was trained on the GladNet dataset, known for its diverse collection of low-light images, tested against the Low-Light dataset, and evaluated using the ExDark dataset for downstream tasks, demonstrating competitive performance with a PSNR of 19.7216 and SSIM of 0.8213.Comment: Accepted by Signal, Image and Video Processin

Optimization of Synthesis of Biomorphic Cellular Iron Oxide from Wood Templates

International audienc

Gas Sensing Properties of Biotemplated Porous ZnO

International audienc

Hierarchically porous ZnO with high sensitivity and selectivity to H2S derived from biotemplates

International audienceHierarchical porous wood-templated ZnO has been successfully synthesized using Lauan and Fir woods as template through a simple hydrothermal bioinspired approach. The template type and calcination temperature in the preparation process have a large effect on the morphologies and porous structures of ZnO according to FESEM, TEM, mercury porosimetry and N2 adsorption investigations. The gas sensing performances of wood-templated and non-templated ZnO were investigated using H2, CO, H2S, NH3, Formaldehyde, Methanol, Ethanol, Acetone, and Isobutene. The article studies the effects of wood template, calcination temperature, and working temperature of gas flow on the gas sensitivity and selectivity in detail. It is revealed that wood-templated ZnO has excellent sensitivity and selectivity to H2S due to inheritance of wood's hierarchical porous structure. The sensing response to H2S of Fir-templated ZnO is about 5.1 times higher than that of non-templated ZnO. Fir-templated ZnO calcined at 600 °C, has the best sensing properties including the highest gas sensing response, the highest selectivity coefficients of H2S and the shortest response and recovery time. The selective sensing mechanism has been discussed from some key aspects, such as gas properties, gas–solid reactions, grain size and hierarchical porous microstructures

Using Frequency Attention to Make Adversarial Patch Powerful Against Person Detector

Deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, object detectors may be attacked by applying a particular adversarial patch to the image. However, because the patch shrinks during preprocessing, most existing approaches that employ adversarial patches to attack object detectors would diminish the attack success rate on small and medium targets. This paper proposes a Frequency Module(FRAN), a frequency-domain attention module for guiding patch generation. This is the first study to introduce frequency domain attention to optimize the attack capabilities of adversarial patches. Our method increases the attack success rates of small and medium targets by 4.18% and 3.89%, respectively, over the state-of-the-art attack method for fooling the human detector while assaulting YOLOv3 without reducing the attack success rate of big targets.Comment: 10pages, 4 figure

STDC-MA Network for Semantic Segmentation

Semantic segmentation is applied extensively in autonomous driving and intelligent transportation with methods that highly demand spatial and semantic information. Here, an STDC-MA network is proposed to meet these demands. First, the STDC-Seg structure is employed in STDC-MA to ensure a lightweight and efficient structure. Subsequently, the feature alignment module (FAM) is applied to understand the offset between high-level and low-level features, solving the problem of pixel offset related to upsampling on the high-level feature map. Our approach implements the effective fusion between high-level features and low-level features. A hierarchical multiscale attention mechanism is adopted to reveal the relationship among attention regions from two different input sizes of one image. Through this relationship, regions receiving much attention are integrated into the segmentation results, thereby reducing the unfocused regions of the input image and improving the effective utilization of multiscale features. STDC- MA maintains the segmentation speed as an STDC-Seg network while improving the segmentation accuracy of small objects. STDC-MA was verified on the verification set of Cityscapes. The segmentation result of STDC-MA attained 76.81% mIOU with the input of 0.5x scale, 3.61% higher than STDC-Seg.Comment: 10 pages, 5 figure

Preparation of Porous Fe from Biomorphic Fe2O3 Precursors with Wood Templates

International audiencePorous iron with woodlike microstructures was prepared from two kinds of wood templates through a developed biotemplating method in the present work. The biomorphic iron oxide was first produced through infiltration and sintering procedures, and was then reduced to the biomorphic iron in a hydrogen atmosphere at high temperatures (600°C and 1000°C). The morphologies of iron and iron oxide products were observed using SEM and FESEM, and their crystal structures were identified using X-ray diffraction. The results indicate that the pure iron phase can be obtained after the reduction process and the products faithfully retained the microstructures of the corresponding wood templates