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Low-light images often suffer from severe visual degradation, affecting both human perception and high-level computer vision tasks. Most existing methods process images in the spatial domain, making it challenging to simultaneously improve brightness while suppressing noise. In this paper, we present a novel perspective to enhance images based on frequency domain characteristics. Specifically, we reveal that the low-frequency components are closely related to luminance and color, whereas the high-frequency components are not. Based on this observation, we propose the Frequency-aware Network (FaNet) for low-light image enhancement. By selectively adjusting low-frequency components, FaNet preserves more high-frequency details while achieving low-light image enhancement. Additionally, we employ a multi-scale framework and selective fusion for effective feature learning and image reconstruction. Experimental results demonstrate the superiority of the proposed method. [Display omitted] • We reveal that the luminance is closely related to low-frequency components. • We design a frequency-aware network to utilize frequency domain features. • A multi-scale framework and selective fusion is proposed for feature learning. [ABSTRACT FROM AUTHOR]

Learning 3D mesh representation is necessary for many computer vision and graphic tasks. Recently, some works have studied convolution methods for directly processing input meshes. However, these methods are usually weak in extracting local geometry information because of the disadvantages such as isotropic filter, neglect of mesh topology, and a small convolution field. In this paper, we introduce a local geometry-perceptive mesh convolution, which pays attention to mesh irregular structures for efficiently capturing geometry features in a multi-ring receptive field. Specifically, we define each template node's dynamic neighbor-attention weights used in multiple attention aggregation operations for obtaining local mesh change information of different vertices in the multi-ring field. After each aggregation, a shared anisotropic filter maps the catenation of each new vertex and its neighbors for extracting geometry features of the current ring. Then, complete local geometry features of each vertex in its large local field are obtained by summing the mapped results of each aggregation. Moreover, the position features of each vertex are added to its local geometry features to get the final representation vector of the vertex. We demonstrate the proposed mesh convolution method's strong ability in modeling 3D mesh shapes. [Display omitted] • Utilizing local irregular mesh structures, dynamic neighbor-attention weights of template's nodes perceive local mesh change information of different vertices. • Multiple attention aggregations and shared anisotropic filters help extract geometry features of large local receptive fields with multi-ring. • The proposed method determines our deep 3D morphable models (3DMMs) have smaller sizes than previous models and achieve state-of-the-art performances. [ABSTRACT FROM AUTHOR]

Low-light image enhancement is crucial in applications such as traffic safety and medical imaging. Besides having characteristics like low luminance and poor visibility, low-light images are inevitably affected by noise. Noise not only covers image details, introduces artifacts, and decreases image quality, but also interferes with downstream computer vision tasks like object detection, image segmentation, and object tracking. Previous methods in image enhancement often overlook noise handling or fail to accurately suppress noise in adaptive denoising processes, resulting in more severe noise artifacts in the enhanced images. To effectively suppress noise, this paper proposes a Channel Self-Attention Based Low-Light Image Enhancement Network (CAENet), which leverages Transformers and CNNs to model long-range and short-range pixel dependencies, extract global and local features, and construct a Noise Suppression Transformer Block that adaptively suppresses noise regions guided by signal-to-noise ratio priors and attention maps. After adaptive noise suppression, the resulting images exhibit fewer noise artifacts and improved details. The experimental results show that the network in this paper outperforms other state-of-the-art methods overall on five representative paired datasets as well as six unpaired datasets, improving the image quality while effectively suppressing the noise. [Display omitted] • To address the problem of diverse noise types and complex interference levels in low-light image enhancement, we propose an end-to-end network CAENet. • Since the severity of noise varies in regions with different luminance, we designed the NSTB based on channel self-attention. • Aiming at the different degree of interference of noise to the whole and details of the image in different sized regions, we design global and local branches. [ABSTRACT FROM AUTHOR]