Because of the unique physical and chemical properties of water, obtaining high-quality underwater images directly is not an easy thing. Hence, recovery and enhancement are indispensable steps in underwater image processing and have therefore become research hotspots. Nevertheless, existing image-processing methods generally have high complexity and are difficult to deploy on underwater platforms with limited computing resources. To tackle this issue, this paper proposes a simple and effective baseline named UIR-Net that can recover and enhance underwater images simultaneously. This network uses a channel residual prior to extract the channel of the image to be recovered as a prior, combined with a gradient strategy to reduce parameters and training time to make the operation more lightweight. This method can improve the color performance while maintaining the style and spatial texture of the contents. Through experiments on three datasets (MSRB, MSIRB and UIEBD-Snow), we confirm that UIR-Net can recover clear underwater images from original images with large particle impurities and ocean light spots. Compared to other state-of-the-art methods, UIR-Net can recover underwater images at a similar or higher quality with a significantly lower number of parameters, which is valuable in real-world applications.

High-dimensional, localized ribonucleic acid (RNA) sequencing is now possible owing to recent developments in spatial transcriptomics (ST). ST is based on highly multiplexed sequence analysis and uses barcodes to match the sequenced reads to their respective tissue locations. ST expression data suffer from high noise and dropout events; however, smoothing techniques have the promise to improve the data interpretability prior to performing downstream analyses. Single-cell RNA sequencing (scRNA-seq) data similarly suffer from these limitations, and smoothing methods developed for scRNA-seq can only utilize associations in transcriptome space (also known as one-factor smoothing methods). Since they do not account for spatial relationships, these one-factor smoothing methods cannot take full advantage of ST data. In this study, we present a novel two-factor smoothing technique, spatial and pattern combined smoothing (SPCS), that employs the k-nearest neighbor (kNN) technique to utilize information from transcriptome and spatial relationships. By performing SPCS on multiple ST slides from pancreatic ductal adenocarcinoma (PDAC), dorsolateral prefrontal cortex (DLPFC) and simulated high-grade serous ovarian cancer (HGSOC) datasets, smoothed ST slides have better separability, partition accuracy and biological interpretability than the ones smoothed by preexisting one-factor methods. Source code of SPCS is provided in Github (https://github.com/Usos/SPCS).

Spatial transcriptomics (ST) encompasses rich multi-modal information related to cell state and organization. Precisely identifying spatial domains with consistent gene expression patterns and histological features is a critical task in ST analysis, which requires comprehensive integration of multi-modal information. Here, we propose TriCLFF, a contrastive learning-based multi-modal feature fusion framework, to effectively integrate spatial associations, gene expression levels, and histological features in a unified manner. Leveraging an advanced feature fusion mechanism, our proposed TriCLFF framework outperforms existing state-of-the-art methods in terms of accuracy and robustness across four datasets (mouse brain anterior, mouse olfactory bulb, human dorsolateral prefrontal cortex, and human breast cancer) from different platforms (10x Visium and Stereo-seq) for spatial domain identification. TriCLFF also facilitates the identification of finer-grained structures in breast cancer tissues and detects previously unknown gene expression patterns in the human dorsolateral prefrontal cortex, providing novel insights for understanding tissue functions. Overall, TriCLFF establishes an effective paradigm for integrating spatial multi-modal data, demonstrating its potential for advancing ST research. The source code of TriCLFF is available online at https://github.com/HBZZ168/TriCLFF.