1 citations · 1 across the 7 of their papers we have counts for
7 papers
CASISR: Circular Arbitrary-Scale Image Super-Resolution
Honggui Li, Zhengyang Zhang, Dingtai Li +11
The generalization performance (GP) of deep learning-based arbitrary-scale image super-resolution (ASISR) methods is subject to limited training datasets and unlimited testing data…
CECGSR: Circular ECG Super-Resolution
Honggui Li, Zhengyang Zhang, Dingtai Li +11
Background and Objective: The electrocardiogram (ECG) plays a crucial role in the diagnosis and treatment of various cardiac diseases. ECG signals suffer from low-resolution (LR) d…
CIC: Circular Image Compression
Honggui Li, Sinan Chen, Dingtai Li +9
Learned image compression (LIC) is currently the cutting-edge method. However, the inherent difference between testing and training images of LIC results in performance degradation…
CMISR: Circular Medical Image Super-Resolution
Honggui Li, Nahid Md Lokman Hossain, Maria Trocan +2
Classical methods of medical image super-resolution (MISR) utilize open-loop architecture with implicit under-resolution (UR) unit and explicit super-resolution (SR) unit. The UR u…
CSwin2SR: Circular Swin2SR for Compressed Image Super-Resolution
Honggui Li, Maria Trocan, Mohamad Sawan +1
Closed-loop negative feedback mechanism is extensively utilized in automatic control systems and brings about extraordinary dynamic and static performance. In order to further impr…
ICRICS: Iterative Compensation Recovery for Image Compressive Sensing
Honggui Li, Maria Trocan, Dimitri Galayko +1
Closed-loop architecture is widely utilized in automatic control systems and attain distinguished performance. However, classical compressive sensing systems employ open-loop archi…
Cascade Decoders-Based Autoencoders for Image Reconstruction
Honggui Li, Dimitri Galayko, Maria Trocan +1
Autoencoders are composed of coding and decoding units, hence they hold the inherent potential of high-performance data compression and signal compressed sensing. The main disadvan…