Linguistics

Philosophy

Neuroscience

Biology

Image quality

Visual attention

Computer graphics (images)

Attention network

Computer science

Brightness

Physics

Segmentation

Pattern recognition (psychology)

Optics

Image (mathematics)

Feature (linguistics)

Performance enhancement

Artificial intelligence

Medicine

Physical medicine and rehabilitation

Image enhancement

Perception

Computer vision

Engineering Applications of Artificial Intelligence

Images captured under low-light conditions usually have poor visual quality, and hence greatly reduce the accuracy of subsequent tasks such as image segmentation and detection. In the low-light image enhancement task, noises in the dark areas are generally amplified while the images' brightness is enhanced. It should be pointed out that many deep learning methods cannot effectively suppress the noise at this stage and capture important feature information. To address the above problem, this paper proposes a Lightweight Attention-guided ConvNeXt Network (LACN) for low-light image enhancement. A novel Attention ConvNeXt Module (ACM) is first proposed by introducing a parameter-free attention module (i.e. SimAM) into the ConvNeXt backbone network. Then, a nontrivial lightweight network LACN based on a multi-attention mechanism is established through stacking two ACMs and fusing their features. In what follows, an improved hybrid attention mechanism, Selective Kernel Attention Module (SKAM), is adopted to effectively extract both global and local information. Such a module realizes the evaluation of lighting conditions for the whole image and the adaptive adjustment of the receptive field. Finally, through the feature fusion module, the features of different stages are aggregated to improve the ability of network to retain color information. Numerous experiments on low-light image enhancement are implemented via comparison with other state-of-the-art methods. Experiments show that the proposed method significantly improves the brightness and contrast of low-illumination images, preserves color information, and suppresses the generation of noises after image brightening.

LACN: A lightweight attention-guided ConvNeXt network for low-light image enhancement