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- EnlightenGAN: Deep Light Enhancement Without Paired Supervision2,636
This paper proposes a highly effective unsupervised generative adversarial network, dubbed EnlightenGAN, that can be trained without low/normal-light image pairs, yet proves to generalize very well on various real-world test images.
- Understanding Convolution for Semantic Segmentation1,998
DUC is designed to generate pixel-level prediction, which is able to capture and decode more detailed information that is generally missing in bilinear upsampling, and a hybrid dilated convolution (HDC) framework in the encoding phase is proposed.
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- Residual Local Feature Network for Efficient Super-Resolution282
The main idea is using three convolutional layers for residual local feature learning to simplify feature aggregation, which achieves a good trade-off between model performance and inference time and won the first place in the runtime track of the NTIRE 2022 efficient super-resolution challenge.
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- Image Super-Resolution via Dual-State Recurrent Networks241
This paper explores new structures for SR based on this compact RNN view, leading to a dual-state design, the Dual-State Recurrent Network (DSRN), which performs favorably against state-of-the-art algorithms in terms of both memory consumption and predictive accuracy.
- Studying Very Low Resolution Recognition Using Deep Networks238
This work forms a dedicated deep learning method that allows for both the flexibility to combat the LR-HR domain mismatch, and the robustness to outliers, and achieves feature enhancement and recognition simultaneously.
- D3: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images229
A Deep Dual-Domain (D3) based fast restoration model to remove artifacts of JPEG compressed images that leverages the large learning capacity of deep networks, as well as the problem-specific expertise that was hardly incorporated in the past design of deep architectures.
- Connecting Image Denoising and High-Level Vision Tasks via Deep Learning200
A convolutional neural network in which convolutions are conducted in various spatial resolutions via downsampling and upsampling operations in order to fuse and exploit contextual information on different scales and the benefit of exploiting image semantics simultaneously for image denoising and high-level vision tasks via deep learning is demonstrated.
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- Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures152
This work trains two-dimensional hierarchical TNs to solve image recognition problems, using a training algorithm derived from the multi-scale entanglement renormalization ansatz, and introduces mathematical connections among quantum many-body physics, quantum information theory, and machine learning.
- Pyramid Attention Network for Image Restoration142
A novel Pyramid Attention module for image restoration, which captures long-range feature correspondences from a multi-scale feature pyramid, and is designed to be able to borrow clean signals from their “clean” correspondences at the coarser levels.
- Experiments on deep learning for speech denoising140
A very lightweight procedure that can predict clean speech spectra when presented with noisy speech inputs is proposed and it is concluded that it can perform better than some comparable single-channel approaches and that it is able to generalize well across various speakers, noise types and signal-to-noise ratios.
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- Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution132
A novel sequential attention branch is proposed, where every pixel is assigned an important factor according to local and global contexts, to enhance high-frequency details in a fast and memory-efficient network.
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- NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results94
The NTIRE 2022 challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images and the aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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