AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This paper proposes a novel low-light image enhancement model that leverages an adaptive enhancement matrix that dynamically fine-tunes enhancement weights based on the unique features of each image, proving its robustness and versatility in real-world applications.
The proposed adaptive enhanced self-reinforced retinex projection (AESRRP) model achieves a competitive performance with the state-of-the-arts methods like URetinex++ and outperforms the existing methods including modern neural networks like UTVNet, and its predecessor ESRRP in quantitative metrics.
Low-light image enhancement aims to improve image quality under insufficient illumination while recovering underlying structural and texture information. Existing approaches, ranging from conventional image processing techniques to deep generative models, have shown promising performance in brightness enhancement and d...
Experimental results demonstrate that AHS can provide a more stable, interpretable, and controllable enhancement of images or videos under cross-scene conditions, offering a reproducible and promising technique for engineering deployment of low-light image and video enhancement.
Low-light image enhancement aims to improve images captured under poor illumination by restoring texture details and correcting color and contrast. While diffusion-based models have demonstrated powerful generative capabilities and significant progress in this task, existing approaches face two empirical challenges. Fi...
Experimental results show that the proposed method provides a convincing quality-efficiency compromise for low-light image enhancement, and it does not require any further training or fine-tuning since the proposed approach is based on a pretrained Deep White Balance model and only uses inference.
A multi-scale image fusion framework is proposed, which leverages adaptive exposure, color gradients, and contrast, and demonstrates consistent improvements over state-of-the-art methods across multiple metrics.
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