Code of Dispel Darkness for Better Fusion: A Controllable Visual Enhancer based on Cross-modal Conditional Adversarial Learning. - HaoZhang1018/DDBF

We propose a controllable visual enhancer, named DDBF, which is based on cross-modal conditional adver-sarial learning and aims to dispel darkness and achieve better visible and infrared modalities fusion. Specifically, a guided restoration module (GRM) is firstly designed to en-hance weakened information in the low-light visible modal-ity.

We propose a controllable visual enhancer, named DDBF, which is based on cross-modal conditional adversarial learning and aims to dispel darkness and achieve be

We propose a controllable visual enhancer named DDBF which is based on cross-modal conditional adversarial learning and aims to dispel darkness and achieve better visible and infrared modalities fusion. Specifically a guided restoration module (GRM) is firstly designed to enhance weakened information in the low-light visible modality.

Code of Dispel Darkness for Better Fusion: A Controllable Visual Enhancer based on Cross-modal Conditional Adversarial Learning. - DDBF/README.md at main · HaoZhang1018/DDBF

Code of Dispel Darkness for Better Fusion: A Controllable Visual Enhancer based on Cross-modal Conditional Adversarial Learning. - DDBF/DDBF_train.py at main · HaoZhang1018/DDBF

2023年9月19日 · 本文是西安交通大学&苏黎世联邦理工学院的赵子祥博士 在ICCV2023上关于多模态 图像融合 的最新工作，题目为：DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion。 本文首次在多模态图像融合领域采用了 扩散模型，很精彩的一篇工作，就是数学推导难住了我这个工科生。

2018年4月19日 · To alleviate this limitation, we propose a deep dynamic boosted forest (DDBF), a novel ensemble algorithm that incorporates the notion of hard example mining into random forest. Specically, we propose to measure the quality of each leaf node of every decision tree in the random forest to determine hard examples.

We propose a controllable visual enhancer named DDBF which is based on cross-modal conditional adversarial learning and aims to dispel darkness and achieve better visible and infrared modalities fusion. Specifically a guided restoration module (GRM) is firstly designed to enhance weakened information in the low-light visible modality.

We propose a controllable visual enhancer, named DDBF, which is based on cross-modal conditional adversarial learning and aims to dispel darkness and achieve better visible and infrared modalities fusion. Specifically, a guided restoration module (GRM) is firstly designed to enhance weakened information in the low-light visible modality.