arXiv subjects

Amara Amara

Publications and source records attributed to Amara Amara.

3 recordsLinked to original sources

arXiv2026

CASISR: Circular Arbitrary-Scale Image Super-Resolution

The generalization performance (GP) of deep learning-based arbitrary-scale image super-resolution (ASISR) methods is subject to limited training datasets and unlimited testing datasets. It is vitally significant to enhance the GP of the pretrained ASISR models by making full use of the testing samples. The ASISR models usually employ an open-loop architecture from low-resolution (LR) images to super-resolution (SR) images. The degradation model from SR samples to LR samples is known bicubic down-sampling for the classical ASISR, is supposed down-sampling with additive random noise for the blind ASISR, and is learnable for the real-world ASISR. Combining the ASISR and degradation models, it is potentially possible to adopt a closed-loop architecture based on the automatic control theory for strengthening the GP of the ASISR methods. Therefore, this paper proposes a closed-loop architecture, circular ASISR (CASISR), to lift the capability of image reconstruction. A mathematical nonlinear loop equation is established to describe the CASISR, the reasonability of the CASISR is proven by conditional probability theory, and the stability of the CASISR is proven by Taylor series approximation. The first-order and second-order absolute difference images are defined to compare the image reconstruction performance of the ASISR and the CASISR methods. Comprehensive simulation experiments show that the proposed CASISR approach outperforms the eight state-of-the-art ASISR approaches in the quality of image reconstruction. Especially, the proposed CASISR is extraordinarily suitable for fractional SR scale factors and is extremely effective for text and stripe images with drastically changed edges.

cs.CV↗

arXiv2025

CECGSR: Circular ECG Super-Resolution

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) due to the use of convenient acquisition devices, as well as internal and external noises and artifacts. Classical ECG super-resolution (ECGSR) methods adopt an open-loop architecture that converts LR ECG signals to super-resolution (SR) ones. According to the theory of automatic control, a closed-loop framework exhibits superior dynamic and static performance compared with its open-loop counterpart. Methods: This paper proposes a closed-loop approach, termed circular ECGSR (CECGSR), which models the degradation process from SR ECG signals to LR ones. The negative feedback mechanism of the closed-loop system is based on the differences between the LR ECG signals. A mathematical loop equation is constructed to characterize the closed-loop infrastructure. The Taylor series expansion is employed to demonstrate the near-zero steady-state error of the proposed method. A Plug-and-Play strategy is considered to establish the SR unit of the proposed architecture, leveraging any existing advanced open-loop ECGSR methods. This paper also presents Transformer model based open-loop ECGSR and closed-loop CECGSR algorithms. Results: Simulation experiments on both noiseless and noisy subsets of the Physikalisch-Technische Bundesanstalt-Extra Large (PTB-XL) datasets demonstrate that the proposed CECGSR outperforms state-of-the-art open-loop ECGSR algorithms in the reconstruction performance of ECG signals. Conclusions: The proposed method will efficiently enrich ECG signal details and remove ECG signal artifacts in clinical applications.

eess.SP↗

arXiv2024

CIC: Circular Image Compression

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 to some extent. Especially for out-of-sample, out-of-distribution, or out-of-domain testing images, the performance of LIC degrades significantly. Classical LIC is a serial image compression (SIC) approach that utilizes an open-loop architecture with serial encoding and decoding units. Nevertheless, according to the principles of automatic control systems, a closed-loop architecture holds the potential to improve the dynamic and static performance of LIC. Therefore, a circular image compression (CIC) approach with closed-loop encoding and decoding elements is proposed to minimize the gap between testing and training images and upgrade the capability of LIC. The proposed CIC establishes a nonlinear loop equation and proves that steady-state error between reconstructed and original images is close to zero by Taylor series expansion. The proposed CIC method possesses the property of Post-Training and Plug-and-Play which can be built on any existing advanced SIC methods. Experimental results including rate-distortion curves on five public image compression datasets demonstrate that the proposed CIC outperforms eight competing state-of-the-art open-source SIC algorithms in reconstruction capacity. Experimental results further show that the proposed method is suitable for out-of-sample testing images with dark backgrounds, sharp edges, high contrast, grid shapes, or complex patterns.

eess.IV↗