Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement

When enhancing low-light images, many deep learning algorithms are based on the Retinex theory. However, the Retinex model does not consider the corruptions hidden in the dark or introduced by the light-up process. Besides, these methods usually require a tedious multi-stage training pipeline and rely on convolutional neural networks, showing limitations in capturing long-range dependencies. In this paper, we formulate a simple yet principled One-stage Retinex-based Framework (ORF). ORF first estimates the illumination information to light up the low-light image and then restores the corruption to produce the enhanced image. We design an Illumination-Guided Transformer (IGT) that utilizes illumination representations to direct the modeling of non-local interactions of regions with different lighting conditions. By plugging IGT into ORF, we obtain our algorithm, Retinexformer. Comprehensive quantitative and qualitative experiments demonstrate that our Retinexformer significantly outperforms state-of-the-art methods on thirteen benchmarks. The user study and application on low-light object detection also reveal the latent practical values of our method. Code, models, and results are available at https://github.com/caiyuanhao1998/RetinexformerComment: ICCV 2023; The first Transformer-based method for low-light image enhancemen

Binarized Spectral Compressive Imaging

Existing deep learning models for hyperspectral image (HSI) reconstruction achieve good performance but require powerful hardwares with enormous memory and computational resources. Consequently, these methods can hardly be deployed on resource-limited mobile devices. In this paper, we propose a novel method, Binarized Spectral-Redistribution Network (BiSRNet), for efficient and practical HSI restoration from compressed measurement in snapshot compressive imaging (SCI) systems. Firstly, we redesign a compact and easy-to-deploy base model to be binarized. Then we present the basic unit, Binarized Spectral-Redistribution Convolution (BiSR-Conv). BiSR-Conv can adaptively redistribute the HSI representations before binarizing activation and uses a scalable hyperbolic tangent function to closer approximate the Sign function in backpropagation. Based on our BiSR-Conv, we customize four binarized convolutional modules to address the dimension mismatch and propagate full-precision information throughout the whole network. Finally, our BiSRNet is derived by using the proposed techniques to binarize the base model. Comprehensive quantitative and qualitative experiments manifest that our proposed BiSRNet outperforms state-of-the-art binarization methods and achieves comparable performance with full-precision algorithms. Code and models are publicly available at https://github.com/caiyuanhao1998/BiSCI and https://github.com/caiyuanhao1998/MSTComment: NeurIPS 2023; The first work to study binarized spectral compressive imaging reconstruction proble

ITportrait: Image-Text Coupled 3D Portrait Domain Adaptation

Domain adaptation of 3D portraits has gained more and more attention. However, the transfer mechanism of existing methods is mainly based on vision or language, which ignores the potential of vision-language combined guidance. In this paper, we propose a vision-language coupled 3D portraits domain adaptation framework, namely Image and Text portrait (ITportrait). ITportrait relies on a two-stage alternating training strategy. In the first stage, we employ a 3D Artistic Paired Transfer (APT) method for image-guided style transfer. APT constructs paired photo-realistic portraits to obtain accurate artistic poses, which helps ITportrait to achieve high-quality 3D style transfer. In the second stage, we propose a 3D Image-Text Embedding (ITE) approach in the CLIP space. ITE uses a threshold function to adaptively control the optimization direction of image or text in the CLIP space. Comprehensive quantitative and qualitative results show that our ITportrait achieves state-of-the-art (SOTA) results and benefits downstream tasks. All source codes and pre-trained models will be released to the public

RFormer: Transformer-based Generative Adversarial Network for Real Fundus Image Restoration on A New Clinical Benchmark

Ophthalmologists have used fundus images to screen and diagnose eye diseases. However, different equipments and ophthalmologists pose large variations to the quality of fundus images. Low-quality (LQ) degraded fundus images easily lead to uncertainty in clinical screening and generally increase the risk of misdiagnosis. Thus, real fundus image restoration is worth studying. Unfortunately, real clinical benchmark has not been explored for this task so far. In this paper, we investigate the real clinical fundus image restoration problem. Firstly, We establish a clinical dataset, Real Fundus (RF), including 120 low- and high-quality (HQ) image pairs. Then we propose a novel Transformer-based Generative Adversarial Network (RFormer) to restore the real degradation of clinical fundus images. The key component in our network is the Window-based Self-Attention Block (WSAB) which captures non-local self-similarity and long-range dependencies. To produce more visually pleasant results, a Transformer-based discriminator is introduced. Extensive experiments on our clinical benchmark show that the proposed RFormer significantly outperforms the state-of-the-art (SOTA) methods. In addition, experiments of downstream tasks such as vessel segmentation and optic disc/cup detection demonstrate that our proposed RFormer benefits clinical fundus image analysis and applications. The dataset, code, and models are publicly available at https://github.com/dengzhuo-AI/Real-FundusComment: IEEE J-BHI 2022; The First Benchmark and First Transformer-based Method for Real Clinical Fundus Image Restoratio

3D Face Arbitrary Style Transfer

Style transfer of 3D faces has gained more and more attention. However, previous methods mainly use images of artistic faces for style transfer while ignoring arbitrary style images such as abstract paintings. To solve this problem, we propose a novel method, namely Face-guided Dual Style Transfer (FDST). To begin with, FDST employs a 3D decoupling module to separate facial geometry and texture. Then we propose a style fusion strategy for facial geometry. Subsequently, we design an optimization-based DDSG mechanism for textures that can guide the style transfer by two style images. Besides the normal style image input, DDSG can utilize the original face input as another style input as the face prior. By this means, high-quality face arbitrary style transfer results can be obtained. Furthermore, FDST can be applied in many downstream tasks, including region-controllable style transfer, high-fidelity face texture reconstruction, large-pose face reconstruction, and artistic face reconstruction. Comprehensive quantitative and qualitative results show that our method can achieve comparable performance. All source codes and pre-trained weights will be released to the public

Quantitative Study on the Law of Surface Subsidence Zoning in Steeply Inclined Extra-Thick Coal Seam Mining

The damage of overlying strata and ground surface caused by the one-time mining space is relatively severe in steeply inclined extra-thick coal seams. The unique law of surface subsidence at these conditions is still missing. Taking Huating Dongxia Coal Mine as the research background, this paper reveals the law-governing effects on rock strata and surface movement and deformation caused by steeply inclined extra-thick coal seam mining with different coal seam dip angles and coal thicknesses by using the methods of surface measurement, theoretical analysis, and numerical simulation. Based on the characteristics of the surface inclination deformation, the surface is divided into four areas along the tendency section line—namely, an outcrop discontinuous deformation area, an overall subsidence area, a gradual subsidence area, and a slight subsidence area. The results show that the influence of the coal seam dip angle on surface subsidence zoning in steeply inclined and thick coal seams is mainly reflected in the affected area range and the form of damage. Coal thickness has a weak effect on the form of rock strata damage and surface movement. Utilizing the influence of the coal seam dip angle and coal seam thickness on the change in the surface subsidence zoning, the calculation formulas for each area range and zoning angle in relation to the coal seam dip angle, coal thickness, mining depth, and vertical stage height are established. The research results can provide a reference to evaluate the influence of mining, especially in steeply inclined extra-thick coal seams

A facile synthesis of hierarchical porous carbon derived from renewable lignin for high-performance supercapacitor

In this work, we proposed a facile one-pot pyrolysis method to conveniently manufacture lignin-derived carbon materials with graded porous construction for use in supercapacitors. The renewable lignin was selected as precursor, while the potassium citrate was used as a pore-forming agent. The properties of the prepared lignin-derived carbon (LAC) and the performance for supercapacitor application were thoroughly evaluated. The LAC at optimal preparation conditions shows a layered porous structure with a large specific surface area of 3174 cm2 g−1 and pore volume of 2.796 cm3 g−1, where the specific capacitance reach to 241 F g−1 at 1 A g−1 scan rate in 6 M KOH electrolyte solution. At the same time, the specific capacitance remains at 220 F g−1 even at an excessive scan velocity of 20 A g−1, while the capacitance retention is still close to 91.3%. The capacitance retention rate is stable above 95% after 10,000 charge/discharge cycles, which shows the desired long-time stability. All these results demonstrate the outstanding properties of the new prepared LAC material and the considerable application potential in the field of electrical energy storage

Using Imagination to Overcome Fear: How Mental Simulation Nudges Consumers’ Purchase Intentions for Upcycled Food

Upcycled food, a new kind of food, provides an effective solution to reduce the food waste from the source on the premise of food security for human beings. However, the commercial success of upcycled food and its contribution to environmental sustainability are determined by consumers’ purchase intentions. In order to overcome consumers’ unfamiliarity with upcycled food and fear of new technology, based on the cue utility theory, we adopted scenario simulation through online questionnaires in three experiments to explore how mental simulation can improve consumers’ product evaluation and purchase intentions for upcycled food. Through ANOVA, the t-test, and the Bootstrap methods, the results showed that, compared with the control group, consumers’ product evaluation and purchase intentions for upcycled food in the mental simulation group significantly increased. Among them, consumers’ inspiration played a mediation role. The consumers’ future self-continuity could moderate the effect of mental simulation on consumers’ purchase intentions for upcycled food. The higher the consumers’ future self-continuity, the stronger the effect of mental simulation. Based on the above results, in the marketing promotion of upcycled food, promotional methods, such as slogans and posters, could be used to stimulate consumers, especially the mental simulation thinking mode of consumer groups with high future self-continuity, thus improving consumers’ purchase intentions for upcycled food

Using Imagination to Overcome Fear: How Mental Simulation Nudges Consumers’ Purchase Intentions for Upcycled Food

Upcycled food, a new kind of food, provides an effective solution to reduce the food waste from the source on the premise of food security for human beings. However, the commercial success of upcycled food and its contribution to environmental sustainability are determined by consumers’ purchase intentions. In order to overcome consumers’ unfamiliarity with upcycled food and fear of new technology, based on the cue utility theory, we adopted scenario simulation through online questionnaires in three experiments to explore how mental simulation can improve consumers’ product evaluation and purchase intentions for upcycled food. Through ANOVA, the t-test, and the Bootstrap methods, the results showed that, compared with the control group, consumers’ product evaluation and purchase intentions for upcycled food in the mental simulation group significantly increased. Among them, consumers’ inspiration played a mediation role. The consumers’ future self-continuity could moderate the effect of mental simulation on consumers’ purchase intentions for upcycled food. The higher the consumers’ future self-continuity, the stronger the effect of mental simulation. Based on the above results, in the marketing promotion of upcycled food, promotional methods, such as slogans and posters, could be used to stimulate consumers, especially the mental simulation thinking mode of consumer groups with high future self-continuity, thus improving consumers’ purchase intentions for upcycled food