Indirect Domain Shift for Single Image Dehazing
Despite their remarkable expressibility, convolution neural networks (CNNs) still fall short of delivering satisfactory results on single image dehazing, especially in terms of faithful recovery of fine texture details. In this paper, we argue that the inadequacy of conventional CNN-based dehazing methods can be attributed to the fact that the domain of hazy images is too far away from that of clear images, rendering it difficult to train a CNN for learning direct domain shift through an end-to-end manner and recovering texture details simultaneously. To address this issue, we propose to add explicit constraints inside a deep CNN model to guide the restoration process. In contrast to direct learning, the proposed mechanism shifts and narrows the candidate region for the estimation output via multiple confident neighborhoods. Therefore, it is capable of consolidating the expressibility of different architectures, resulting in a more accurate indirect domain shift (IDS) from the hazy images to that of clear images. We also propose two different training schemes, including hard IDS and soft IDS, which further reveal the effectiveness of the proposed method. Our extensive experimental results indicate that the dehazing method based on this mechanism outperforms the state-of-the-arts.
Code (0)
등록된 구현이 없습니다.
Tasks
Image DehazingSingle Image DehazingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
FWB-Net:Front White Balance Network for Color Shift Correction in Single Image Dehazing via Atmospheric Light Estimation
In recent years, single image dehazing deep models based on Atmospheric Scattering Model (ASM) have achieved remarkable results. But the dehazing outputs of those models suffer from color shift. Analyzing the ASM model s…
Image DehazingSingle Image DehazingDAMix: A Density-Aware Mixup Augmentation for Single Image Dehazing under Domain Shift
Deep learning-based methods have achieved considerable success on single image dehazing in recent years. However, these methods are often subject to performance degradation when domain shifts are confronted. Specifically…
Data AugmentationDomain AdaptationImage DehazingSingle Image Dehazing+1Domain Adaptation for Image Dehazing
Image dehazing using learning-based methods has achieved state-of-the-art performance in recent years. However, most existing methods train a dehazing model on synthetic hazy images, which are less able to generalize wel…
Domain AdaptationImage DehazingTranslationFrom Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data
Single image dehazing is a challenging task, for which the domain shift between synthetic training data and real-world testing images usually leads to degradation of existing methods. To address this issue, we propose a …
Image DehazingSingle Image DehazingDehazeMamba: SAR-guided Optical Remote Sensing Image Dehazing with Adaptive State Space Model
Optical remote sensing image dehazing presents significant challenges due to its extensive spatial scale and highly non-uniform haze distribution, which traditional single-image dehazing methods struggle to address effec…
Image DehazingSemantic SegmentationSingle Image Dehazing