Low Light Image Enhancement via Global and Local Context Modeling
Images captured under low-light conditions manifest poor visibility, lack contrast and color vividness. Compared to conventional approaches, deep convolutional neural networks (CNNs) perform well in enhancing images. However, being solely reliant on confined fixed primitives to model dependencies, existing data-driven deep models do not exploit the contexts at various spatial scales to address low-light image enhancement. These contexts can be crucial towards inferring several image enhancement tasks, e.g., local and global contrast, brightness and color corrections; which requires cues from both local and global spatial extent. To this end, we introduce a context-aware deep network for low-light image enhancement. First, it features a global context module that models spatial correlations to find complementary cues over full spatial domain. Second, it introduces a dense residual block that captures local context with a relatively large receptive field. We evaluate the proposed approach using three challenging datasets: MIT-Adobe FiveK, LoL, and SID. On all these datasets, our method performs favorably against the state-of-the-arts in terms of standard image fidelity metrics. In particular, compared to the best performing method on the MIT-Adobe FiveK dataset, our algorithm improves PSNR from 23.04 dB to 24.45 dB.
Code (0)
등록된 구현이 없습니다.
Tasks
Image EnhancementLow-Light Image EnhancementMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Adaptive Low Light Enhancement via Joint Global-Local Illumination Adjustment
Images captured under real-world low-light conditions face significant challenges due to uneven ambient lighting, making it difficult for existing end-to-end methods to enhance images with a large dynamic range to normal…
Local Low-light Image Enhancement via Region-Aware Normalization
In the realm of Low-Light Image Enhancement (LLIE), existing research primarily focuses on enhancing images globally. However, many applications require local LLIE, where users are allowed to illuminate specific regions …
Image EnhancementLow-Light Image EnhancementPPformer: Using pixel-wise and patch-wise cross-attention for low-light image enhancement
Recently, transformer-based methods have shown strong competition compared to CNN-based methods on the low-light image enhancement task, by employing the self-attention for feature extraction. Transformer-based methods p…
Image EnhancementLow-Light Image EnhancementBilateral Interaction for Local-Global Collaborative Perception in Low-Light Image Enhancement
Low-light image enhancement is a challenging task due to the limited visibility in dark environments. While recent advances have shown progress in integrating CNNs and Transformers, the inadequate local-global perceptual…
Image EnhancementLow-Light Image EnhancementLow-light Image Enhancement Using the Cell Vibration Model
Low light very likely leads to the degradation of an image's quality and even causes visual task failures. Existing image enhancement technologies are prone to overenhancement, color distortion or time consumption, and t…
Image EnhancementLow-Light Image Enhancement