As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spatial neighbors and clustering centers. To address this issue, an improved FCM algorithm based on morphological reconstruction and membership filtering (FRFCM) that is significantly faster and more robust than FCM is proposed in this paper. First, the local spatial information of images is incorporated into FRFCM by introducing morphological reconstruction operation to guarantee noise-immunity and image detail-preservation. Second, the modification of membership partition, based on the distance between pixels within local spatial neighbors and clustering centers, is replaced by local membership filtering that depends only on the spatial neighbors of membership partition. Compared with stateof-the-art algorithms, the proposed FRFCM algorithm is simpler and significantly faster, since it is unnecessary to compute the distance between pixels within local spatial neighbors and clustering centers. In addition, it is efficient for noisy image segmentation because membership filtering are able to improve membership partition matrix efficiently. Experiments performed on synthetic and real-world images demonstrate that the proposed algorithm

A great number of improved fuzzy c-means (FCM) clustering algorithms have been widely used for grayscale and color image segmentation. However, most of them are timeconsuming and unable to provide desired segmentation results for color images due to two reasons. The first one is that the incorporation of local spatial information often causes a high computational complexity due to the repeated distance computation between clustering centers and pixels within a local neighboring window. The other one is that a regular neighboring window usually breaks up the real local spatial structure of images and thus leads to a poor segmentation. In this work, we propose a superpixel-based fast FCM clustering algorithm (SFFCM) that is significantly faster and more robust than stateof-the-art clustering algorithms for color image segmentation. To obtain better local spatial neighborhoods, we firstly define a multiscale morphological gradient reconstruction (MMGR) operation to obtain a superpixel image with accurate contour. In contrast to traditional neighboring window of fixed size and shape, the superpixel image provides better adaptive and irregular local spatial neighborhoods that are helpful for improving color image segmentation. Secondly, based on the obtained superpixel image, the original color image is simplified efficiently and its histogram is computed easily by counting the number of pixels in each region of the superpixel image. Finally, we implement FCM with histogram parameter on the superpixel image to obtain the final segmentation result. Experiments performed on synthetic images and real images demonstrate that the proposed algorithm provides better segmentation results and takes less time than state-of-the-art clustering algorithms for color image segmentation.

Here, a low-temperature solution-processed nickel oxide (NiOx) thin film was first employed as a hole transport layer in both inverted (p-i-n) planar and regular (n-i-p) mesoscopic organic–inorganic hybrid perovskite solar cells (PVSCs).

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Interfacial solar steam generation (ISSG) has received increasing attention in both industry and academia, and is considered a method with great potential for wastewater treatment and desalination. These practical applications require materials that fulfil several requirements: being low cost, being scalable in terms of processing, being environmentally friendly, and having a high, stable optical–thermal conversion efficiency of solar vaporization. Currently, biomass materials show very promising prospects for ISSG systems. Here, it is observed that bamboo charcoal (BC) possesses a series of unique advantages that make it a highly efficient ISSG device. The broadband light absorption and the arched and porous structural features of BC fulfil all the basic requirements of an ISSG device in localized heating, heat management, and water supply. The self‐contained arched BC device demonstrates high evaporation efficiency (84% under 1 sun radiation) and superb stability under strongly acidic, strongly alkaline, and intense light environment conditions. More importantly, the porous BC device can provide stable fresh water production that simultaneously promotes the purification of water via evaporation by heating. Finally, the low cost, environmentally sustainable, mechanically robust, and long‐term stable BC device is a potential opportunity for wastewater treatment and desalination in underdeveloped areas.

Superhydrophilic porous carbon foam was successfully synthesized by facile carbonization of potato, providing a new perspective to design self-desalting monolithic ISSG to satisfy the demand for highly efficient and enduring solar desalination.

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Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A comprehensive thematic survey on medical image segmentation using deep learning techniques is presented. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi‐level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyse literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches.