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- Image demosaicing: a systematic survey424
A systematic survey of over seventy published works in this field since 1999 is provided, which suggests most existing works belong to the class of sequential demosaicing - i.e., luminance channel is interpolated first and then chrominance channels are reconstructed based on recovered luminance information.
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- Aspect Term Extraction with History Attention and Selective Transformation290
Experimental results over four benchmark datasets clearly demonstrate that the new framework for tackling ATE can outperform all state-of-the-art methods.
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- LIQA: Lifelong Blind Image Quality Assessment87
This work proposes a novel Lifelong blind Image Quality Assessment (LIQA) approach, targeting to achieve the lifelong learning of BIQA, and adopts the Split-and-Merge distillation strategy to train a single-head network that makes task-agnostic predictions.
- Learned Block-Based Hybrid Image Compression74
This paper introduces explicit intra prediction into a learned image compression framework to utilize the relation among adjacent blocks and proposes a contextual prediction module (CPM) to better capture long-range correlations by utilizing the strip pooling to extract the most relevant information in neighboring latent space, thus achieving effective information prediction.
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- Task-Driven Semantic Coding via Reinforcement Learning54
This work designs semantic maps for different tasks to extract the pixelwise semantic fidelity for videos/images and implements task-driven semantic coding by implementing semantic bit allocation based on reinforcement learning (RL).
- Learning Omni-Frequency Region-adaptive Representations for Real Image Super-Resolution53
This paper designs a Frequency Decomposition module to separate different frequency components to comprehensively compensate the information lost for real LR image and designs a Region-adaptive Frequency Aggregation module by leveraging dynamic convolution and spatial attention to adaptively restore frequency components for different regions.
- Learning Disentangled Feature Representation for Hybrid-Distorted Image Restoration50
This work introduces the concept of Disentangled Feature Learning to achieve the feature-level divide-and-conquer of hybrid distortions and proposes the feature disentanglement module (FDM) to distribute feature representations of different distortions into different channels by revising gain-control-based normalization.
- Convolutional‐capsule network for gastrointestinal endoscopy image classification45
The core of the method is a lesion‐aware CNN feature extraction module which can encode sufficiently detailed information of lesions in midlevel CNN features and in turn enable the subsequent capsule classification network to effectively learn deformation‐invariant relationships between image entities.
- NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results43
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results, and gauge the state of the art of video quality enhancement.
- AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results43
This paper introduces the real image Super-Resolution challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020, and gauges the state-of-the-art approaches for real image SR in terms of PSNR and SSIM.
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- PromptCIR: Blind Compressed Image Restoration with Prompt Learning39
A prompt-learning-based compressed image restoration network, dubbed PromptCIR, which can effectively restore images from various compress levels, and exploits prompts to encode compression information implicitly, where prompts directly interact with soft weights generated from image features, thus providing dynamic content-aware and distortion-aware guidance for the restoration process.
- Learning Distortion Invariant Representation for Image Restoration from a Causality Perspective33
This paper is the first to propose a novel training strategy for image restoration from the causality perspective, to improve the generalization ability of DNNs for unknown degradations.
- FAN: Frequency Aggregation Network for Real Image Super-Resolution32
This work proposes FAN, a frequency aggregation network, to address the real-world image super-resolu-tion problem by extracting different frequencies of the LR image and passing them to a channel attention-grouped residual dense network (CA-GRDB) individually to output corresponding feature maps.
- LIRA: Lifelong Image Restoration from Unknown Blended Distortions27
Experimental results show that the proposed approach can not only achieve state-of-the-art performance on blended distortions removal tasks in both PSNR/SSIM metrics, but also maintain old expertise while learning new restoration tasks.
- StyleAM: Perception-Oriented Unsupervised Domain Adaption for No-Reference Image Quality Assessment26
This work proposes an effective perception-oriented unsupervised domain adaptation method StyleAM (Style Alignment and Mixup) for NR-IQA, which transfers sufficient knowledge from label-rich source domain data to label-free target domain images.
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- Multi-scale Grouped Dense Network for VVC Intra Coding24
The multi-scale grouped dense network (MSGDN) is designed to further reduce the compression artifacts by combining the multi- scale and grouped dense block, which are integrated as the post-process network of VVC intra coding.
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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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