It is highly desired but challenging to acquire high-quality photos with clear content in low-light environments. Although multi-image processing methods (using burst, dual-exposure, or multi-exposure images) have made significant progress in addressing this issue, they typically focus on specific restoration or enhancement problems, and do not fully explore the potential of utilizing multiple images. Motivated by the fact that multi-exposure images are complementary in denoising, deblurring, high dynamic range imaging, and super-resolution, we propose to utilize exposure bracketing photography to get a high-quality image by combining these tasks in this work. Due to the difficulty in collecting real-world pairs, we suggest a solution that first pre-trains the model with synthetic paired data and then adapts it to real-world unlabeled images. In particular, a temporally modulated recurrent network (TMRNet) and self-supervised adaptation method are proposed. Moreover, we construct a data simulation pipeline to synthesize pairs and collect real-world images from 200 nighttime scenarios. Experiments on both datasets show that our method performs favorably against the state-of-the-art multi-image processing ones. Code and datasets are available at https://github.com/cszhilu1998/BracketIRE.

Gastroscopy and colonoscopy have become the fundamental tools for gastrointestinal (GI) tract diagnosis and treatment. Conventional tethered devices usually lead to the use of anesthetic agents and patient discomfort. Capsule endoscopy is becoming an ideal alternative, however, the smooth capsule shape may hinder its active locomotion and retention in the GI tract. Here, we propose a spherical soft magnetic millirobot (S2M2robot) that integrates a virus-like spherical body with protrusions and an octopus-inspired sucker design for enhanced physical capabilities. The protruding suckers (radius: 2.2-4.8 mm) on its surface enable efficient locomotion performance (maximum angular velocity: 8 r/s, maximum speed: 180 mm/s) with strong adhesive ability (maximum force: 3.5 N). The ex vivo experiments in a swine stomach demonstrate the robot’s motion on the slippery surface of the gastric mucosa and the effectiveness of the pressure-based drug delivery system. The in vitro and ex vivo results highlight the superior mobility and controllability, showcasing its potential as a carrier robot for the next-generation capsule endoscopy.

By imposing a single hierarchy on data, XML makes queries brittle in the sense that a query might fail to produce the desired result if it is executed on the same data organized in a different hierarchy, or if the hierarchy evolves during the lifetime of an application. This paper presents a new transformation language, called XMorph, which supports more flexible querying. XMorph is a shape polymorphic language, that is, a single XMorph query can extract and transform data from differently-shaped hierarchies. The XMorph data shredder distills XML data into a graph of closest relationships, which are exploited by the query evaluation engine to produce a result in the shape specified by an XMorph query.

Path expressions are the principal means of locating data in a hierarchical model. But path expressions are brittle because they often depend on the structure of data and break if the data is structured differently. The structure of data could be unfamiliar to a user, may differ within a data collection, or may change over time as the schema evolves. This paper proposes a novel construct that locates related nodes in an instance of an XML data model, independent of a specific structure. It can augment many XPath expressions and can be seamlessly incorporated in XQuery or XSLT.