No-Reference Image Sharpness Assessment in Autoregressive Parameter Space

By Ke Gu, Guangtao Zhai, Weisi Lin, Xiaokang Yang, Wenjun Zhang. IEEE Transactions on Image Processing, 2015

307 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/9216981e10d95bf7f9044651ae24ecb294de044f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Ke Gu
3. Xiaokang Yang
4. Weisi Lin
5. Guangtao Zhai
6. Wenhan Yang
7. Jiaying Liu
8. Xiongkuo Min

In Media Technology

1. Ke Gu
2. Weisi Lin
3. Guangtao Zhai
4. Xiaokang Yang
5. Dacheng Tao
6. Xiongkuo Min
7. J. Qiao
8. Wenhan Yang

Citations in the corpus

• Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data (2018, background): “Actually, these years have seen quite a few works dedicated to sharpness assessment [50, 51, 52].”
• No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization (2017, unknown): “SRCC measuring the prediction monotonicity exactly illustrates this, and thus it is usually treated as one of the most important indices in the IQA [2], [7], [20], [35].”
• Blind Quality Assessment Based on Pseudo-Reference Image (2018, background): “[26] estimated sharpness in an autoregressive parameter space.”
• LR3M: Robust Low-Light Enhancement via Low-Rank Regularized Retinex Model (2020, background): “Namely, blind tone-mapped quality index (BTMQI) [44], no-reference image quality metric for contrast distortion (NIQMC) [45] and blind sharpness metric in the autoregressive parameter space (ARISMC) [46] to evaluate the enhancement results comprehensively.”
• An Experiment-Based Review of Low-Light Image Enhancement Methods (2020, methodology): “…image quality index (BTMQI) [291], gradient ratio-ing at visible edges (GRVE) [292], the autoregressive-based image sharpness metric (ARISM) [293], the no-reference image quality metric for contrast distortion (NIQMC) [294], the Global Contrast Factor (GCF) [295], the average information…”
• Low-Light Image Enhancement With Semi-Decoupled Decomposition (2020, methodology): “The evaluation metrics include Natural Image Quality Evaluator (NIQE) [38], AutoRegressive-based Image Sharpness Metric (ARISM) [39] and No-reference Free Energy based Robust Metric (NFERM) [40].”