• Propose a novel noise-reduction-oriented super-resolution framework tailored for precision agriculture applications. • Introduce a Gaussian-based saliency map generation method to guide object-aware SR reconstruction. Design a multitask learning architecture that jointly predicts high-resolution images and saliency maps to enhance edge and contour understanding. • Integrate a saliency-guided segmentation post-processing technique to automatically select optimally enhanced high-resolution outputs. • Achieve state-of-the-art PSNR performance in noise reduction across four benchmark datasets and one agriculture-related dataset. Low-resolution images significantly degrade the performance of vision systems in real-world settings. While super-resolution techniques improve image details, they often introduce additional noise, complicating tasks like detection and recognition. Thus, it is crucial to develop methods that enhance image quality while reducing noise. This paper presents a novel solution focused on noise reduction, integrating Super-Resolution (SR) reconstruction, Multitask Learning (MTL), and Saliency-Guided Segmentation (SGS) for effective post-processing. When generating high-resolution images, existing SR methods struggle with noise at object boundaries and edges. Our model learns a supplementary task to create blending saliency objects into high-resolution images, thus improving the understanding of object boundaries, edges, and contours, while leading to notable noise reduction. Experimental results show that SUNRISE achieves the highest PSNR on benchmark datasets, including SET5 (33.44 dB), SET14 (32.17 dB), BSD100 (31.87 dB), and Urban100 (31.81 dB), outperforming state-of-the-art methods such as FxSR, DualFormer, SROOE, and WGSR by up to 1.5 dB. On the Saffron Flower Dataset, SUNRISE achieves a PSNR of 33.36, an SSIM of 0.824, MSE of 35.957, and LPIPS of 0.232, demonstrating superior pixel-level fidelity, structural preservation, and perceptual similarity in agricultural images. These results confirm that SUNRISE effectively reduces noise while preserving fine details, thereby enhancing downstream vision tasks like object detection. The code is available at https://github.com/tmtgssi/SUNRISE

Robotic systems are becoming pervasive and adopted in increasingly many domains, such as manufacturing, healthcare, and space exploration. To this end, engineering software has emerged as a crucial discipline for building maintainable and reusable robotic systems. The field of robotics software engineering research has received increasing attention, fostering autonomy as a fundamental goal. However, robotics developers are still challenged trying to achieve this goal given that simulation is not able to deliver solutions to realistically emulate real-world phenomena. Robots also need to operate in unpredictable and uncontrollable environments, which require safe and trustworthy self-adaptation capabilities implemented in software. Typical techniques to address the challenges are runtime verification, field-based testing, and mitigation techniques that enable fail-safe solutions. However, there is no clear guidance to architect ROS-based systems to enable and facilitate runtime verification and field-based testing. This paper aims to fill in this gap by providing guidelines that can help developers and quality assurance (QA) teams when developing, verifying or testing their robots in the field. These guidelines are carefully tailored to address the challenges and requirements of testing robotics systems in real-world scenarios. We conducted (i) a literature review on studies addressing runtime verification and field-based testing for robotic systems, (ii) mined ROS-based applications repositories, and (iii) validated the applicability, clarity, and usefulness via two questionnaires with 55 answers overall. We contribute 20 guidelines: 8 for developers and 12 for QA teams formulated for researchers and practitioners in robotic software engineering. Finally, we map our guidelines to open challenges thus far in runtime verification and field-based testing for ROS-based systems and, we outline promising research directions in the field.Guidelines website and replication package:https://ros-rvft.github.io.