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Found 5 papers, 2 papers with code
Prompt-based test-time real image dehazing: a novel pipeline
We experimentally find that given a dehazing model trained on synthetic data, by fine-tuning the statistics (i. e., mean and standard deviation) of encoding features, PTTD is able to narrow the domain gap, boosting the performance of real image dehazing.
Accurate and lightweight dehazing via multi-receptive-field non-local network and novel contrastive regularization
Thus, a multi-receptive-field non-local network (MRFNLN) consisting of the multi-stream feature attention block (MSFAB) and cross non-local block (CNLB) is presented in this paper.
DEA-Net: Single image dehazing based on detail-enhanced convolution and content-guided attention
In this paper, a detail-enhanced attention block (DEAB) consisting of the detail-enhanced convolution (DEConv) and the content-guided attention (CGA) is proposed to boost the feature learning for improving the dehazing performance.
Ranked #2 on
Image Dehazing
on Haze4k
Learning Inter- and Intraframe Representations for Non-Lambertian Photometric Stereo
Photometric stereo provides an important method for high-fidelity 3D reconstruction based on multiple intensity images captured under different illumination directions.
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features
Firstly, we proposed a novel Orientation-Aware feature extraction and fusion Module (OAM), which contains a mixture of 1D and 2D convolutional kernels (i. e., 5 x 1, 1 x 5, and 3 x 3) for extracting orientation-aware features.
