Low-light video enhancement is highly demanding in maintaining spatiotemporal color consistency. Therefore, improving the accuracy of color mapping and keeping the latency low are challenging. On this basis, we propose incorporating wavelet-priori for the 4D lookup table (WaveLUT), which effectively enhances the color coherence between video frames and the accuracy of color mapping while maintaining low latency. Specifically, we use the wavelet low-frequency domain to construct an optimized lookup prior and achieve an adaptive enhancement effect through a designed wavelet-prior 4D lookup table. To effectively compensate for the a priori loss in the low light region, we further explore a dynamic fusion strategy that adaptively determines the spatial weights on the basis of the correlation between the wavelet lighting prior and the target intensity structure. In addition, during the training phase, we devise a Fourier-text driven appearance reconstruction method that dynamically balances brightness and content through multimodal semantics-driven Fourier spectra. Extensive experiments on a wide range of benchmark datasets show that this method effectively enhances the previous method's ability to perceive the color space and achieves metric-favourable and perceptually oriented real-time enhancement while maintaining high efficiency. The code is available athttps://github.com/hejh8/WaveLUT.

Low-light image enhancement aims to improve the visibility of degraded images to better align with human visual perception. While diffusion-based methods have shown promising performance due to their strong generative capabilities. However, their unidirectional modelling of degradation often struggles to capture the complexity of real-world degradation patterns, leading to structural inconsistencies and pixel misalignments. To address these challenges, we propose a bidirectional diffusion optimization mechanism that jointly models the degradation processes of both low-light and normal-light images, enabling more precise degradation parameter matching and enhancing generation quality. Specifically, we perform bidirectional diffusion-from low-to-normal light and from normal-to-low light during training and introduce an adaptive feature interaction block (AFI) to refine feature representation. By leveraging the complementarity between these two paths, our approach imposes an implicit symmetry constraint on illumination attenuation and noise distribution, facilitating consistent degradation learning and improving the model's ability to perceive illumination and detail degradation. Additionally, we design a reflection-aware correction module (RACM) to guide color restoration post-denoising and suppress overexposed regions, ensuring content consistency and generating high-quality images that align with human visual perception. Extensive experiments on multiple benchmark datasets demonstrate that our method outperforms state-of-the-art methods in both quantitative and qualitative evaluations while generalizing effectively to diverse degradation scenarios.Code

Efficient Image Super Resolution (EISR) techniques are critical to meeting the real-time demands of resource-constrained devices. Current approaches focus too much on parameter reduction, thus slightly sacrificing image quality. To alleviate this issue, we propose a novel Hybrid Attention Dense Network (HADN) in this paper. HADN fully explores how to balance model performance and image recovery quality better. It incorporates two key designs: (1) We apply Hybrid-domain Attention Dense Blocks (HADB) with different feature layers cumulatively connected to enhance the representation of shallow features. (2) We designed a novel Hybrid-domain Attention Block (HAB) to reinforce the correlation between image edges and receptive fields for effective information fusion to improve the perception of detailed image features. Extensive experimental analyses affirm the competitiveness of the proposed method, showcasing its ability to balance performance and recovery effectively, even with smaller training datasets. The code is available at https://github.com/Yuii666/HADN .

Due to the singularity of real-world paired datasets and the complexity of low-light environments, this leads to supervised methods lacking a degree of scene generalisation. Meanwhile, limited by poor lighting and content guidance, existing zero-shot methods cannot handle unknown severe degradation well. To address this problem, we will propose a new zero-shot low-light enhancement method to compensate for the lack of light and structural information in the diffusion sampling process by effectively combining the wavelet and Fourier frequency domains to construct rich a priori information. The key to the inspiration comes from the similarity between the wavelet and Fourier frequency domains: both light and structure information are closely related to specific frequency domain regions, respectively. Therefore, by transferring the diffusion process to the wavelet low-frequency domain and combining the wavelet and Fourier frequency domains by continuously decomposing them in the inverse process, the constructed rich illumination prior is utilised to guide the image generation enhancement process. Sufficient experiments show that the framework is robust and effective in various scenarios.

Low-lightimage enhancement methods are difficult to fit the complex nonlinear relationship between normal and low-light images due to uneven illumination and noise effects. The recently proposed Kolmogorov-Arnold networks (KANs) feature spline-based convolutional layers and learnable activation functions, which can effectively capture nonlinear dependencies. In this paper, we design a KAN-Block based on KANs and innovatively apply it to low-light image enhancement. This method effectively alleviates the limitations of current methods constrained by linear network structures and lack of interpretability, further demonstrating the potential of KANs in low-level vision tasks. Given the poor perception of current low-light image enhancement methods and the stochastic nature of the inverse diffusion process, we further introduce frequency-domain perception for visually oriented enhancement. Extensive experiments demonstrate the competitive performance of our method on benchmark datasets.

Low-light image enhancement techniques have significantly progressed, but unstable image quality recovery and unsatisfactory visual perception are still significant challenges. To solve these problems, we propose a novel and robust low-light image enhancement method via CLIP-Fourier guided wavelet diffusion, abbreviated as CFWD. Specifically, the CFWD leverages multimodal visual-language information in the frequency domain space created by multiple wavelet transforms to guide the enhancement process. Multiscale supervision across different modalities facilitates the alignment of image features with semantic features during the wavelet diffusion process, effectively bridging the gap between the degraded and normal domains. Moreover, to further promote the effective recovery of the image details, we combine the Fourier transform based on the wavelet transform and construct a hybrid high-frequency perception module (HFPM) with a significant perception of the detailed features. This module avoids the diversity confusion of the wavelet diffusion process by guiding the fine-grained structure recovery of the enhancement results to achieve favourable metrics and perceptually oriented enhancement. Extensive quantitative and qualitative experiments on publicly available real-world benchmarks show that our approach outperforms existing state-of-the-art methods, achieving significant progress in image quality and noise suppression. The project code is available at https://github.com/hejh8/CFWD .

Low-light image enhancement is an essential task in image restoration. Inspired by the diffusion model, the related methods have achieved remarkable results in low-level visual tasks. However, such methods are susceptible to large-scale images, generating problems such as overconsumption of resources and low recovery efficiency. To address this, we propose a detail-guided latent space low-light image enhancement diffusion model called DLDiff. Leveraging the generative power of the latent diffusion model, we explore ways to speed up inference better while producing excellent perceptual fidelity. Specifically, we initially employ the latent diffusion model to transform low-light image features into a latent space representation, thereby reducing computational resource consumption. Next, we design a lightweight detail prompt module that combines cross-convolution and vast-receptive-field convolution blocks. This module enhances the fine-grained details of the image, effectively supplements multiscale feature information, and minimizes feature loss in the latent space. Furthermore, we devise the content-aware loss group to facilitate learning noise and image information, enhancing the model's recovery capability, guiding stable sampling, and constraining diverse content generation. Through extensive experiments, we demonstrate the model's significant efficiency and quality advantages in low-light image enhancement tasks.

In this paper, a comprehensive solution for enabling unmanned aerial vehicle (UAV) to autonomously fly through complex and dynamic environments is proposed. Moving objects all have unique property information, we propose a method that utilizes deep learning for 3D dynamic environment perception, while taking into account limitations in computing resources. For safer dynamic avoidance, we first model the dynamic target and integrate it into a static grid occupancy map, and then construct a gradient field based on its attribute information. To achieve autonomous UAV flight in dynamic environments, we design an adaptive planning method based on gradient optimization, which achieves significant computational savings by autonomously adjusting the planning frequency and using manually constructed gradients instead of maintaining a signed distance field (SDF). We have integrated the above approach into a customised quadrotor system and thoroughly tested it in real-world, verifying its flexibility to handle multiple objects with variable speed motion in complex enviroment.