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- Zhou WangSuggested from co-authorship
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Works109 from public data
- Image Quality Assessment: Unifying Structure and Texture Similarity1,590
This work develops the first full-reference image quality model with explicit tolerance to texture resampling, using a convolutional neural network to construct an injective and differentiable function that transforms images to multi-scale overcomplete representations.
- 1,186
- Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network1,013
A deep bilinear model for blind image quality assessment that works for both synthetically and authentically distorted images and achieves state-of-the-art performance on both synthetic and authentic IQA databases is proposed.
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- Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective465
This work develops a general and automated multitask learning scheme for BIQA to exploit auxiliary knowledge from other tasks, in a way that the model parameter sharing and the loss weighting are determined automatically.
- 444
- Reversibility improved data hiding in encrypted images411
A novel reversible data hiding technique in encrypted images where some pixels are estimated before encryption so that additional data can be embedded in the estimating errors and the data extraction and image recovery are free of errors.
- 362
- Robust Multi-Exposure Image Fusion: A Structural Patch Decomposition Approach355
The proposed algorithm not only outperforms previous MEF algorithms on static scenes but also consistently produces high quality fused images with little ghosting artifacts for dynamic scenes and maintains a lower computational cost compared with the state-of-the-art deghosting schemes.
- Unified Blind Quality Assessment of Compressed Natural, Graphic, and Screen Content Images337
A unified content-type adaptive (UCA) blind image quality assessment model that is applicable across content types and leads to superior performance on the constructed CCT database, and is training-free, implying strong generalizability.
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- Continual Learning for Blind Image Quality Assessment126
This paper formulates continual learning for BIQA, where a model learns continually from a stream of IQA datasets, building on what was learned from previously seen data, and proposes a simple yet effective continual learning method.
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- Locally Adaptive Structure and Texture Similarity for Image Quality Assessment76
A locally adaptive structure and texture similarity index for full-reference IQA, which is term A-DISTS, which relies on a single statistical feature, namely the dispersion index, to localize texture regions at different scales.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. 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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