Photo retouching aims to adjust the hue, luminance, contrast, and saturation of the image to make it more human and aesthetically desirable. Based on researches on image imaging process and artists' retouching processes, we propose three improvements to existing automatic retouching methods. Firstly, in the past retouching methods, all the imaging conditions in EXIF were ignored. According to this, we design a simple module to introduce these imaging conditions into a network called ECM (EXIF Condition Module). This module can improve the performance of several existing auto-retouching methods with only a small parameter cost. Additionally, artists' operations also were ignored. By investigating artists' operations in retouching, we propose a two-stage network that brightens images first and then enriches them in the chrominance plane to mimic artists. Finally, we find that there is a color imbalance in the existing retouching dataset, thus, hue palette loss is designed to resolve the imbalance and make the image more vibrant. Experimental results show that our method is effective on the benchmark MIT-Adobe FiveK dataset and PPR10 K dataset, and achieves SOTA performance in both quantitative and qualitative evaluation.

Underwater images are subject to intricate and diverse degradation, inevitably affecting the effectiveness of underwater visual tasks. However, most approaches primarily operate in the raw pixel space of images, which limits the exploration of the frequency characteristics of underwater images, leading to an inadequate utilization of deep models' representational capabilities in producing high-quality images. In this paper, we introduce a novel Underwater Image Enhancement (UIE) framework, named WF-Diff, designed to fully leverage the character-istics of frequency domain information and diffusion models. WF-Diff consists of two detachable networks: Wavelet-based Fourier information interaction network (WFI2-net) and Frequency Residual Diffusion Adjustment Module (FR-DAM). With our full exploration of the frequency domain in-formation, WFI2-net aims to achieve preliminary enhancement of frequency information in the wavelet space. Our proposed FRDAM can further refine the highand low-frequency information of the initial enhanced images, which can be viewed as a plug-and-play universal module to adjust the detail of the underwater images. With the above techniques, our algorithm can show SOTA performance on real-world underwater image datasets, and achieves competitive performance in visual quality. The code is available at https://github.com/zhihefang/WF-Diff.

Underwater images suffer from complex and diverse degradation, which inevitably affects the performance of underwater visual tasks. However, most existing learning-based underwater image enhancement (UIE) methods mainly restore such degradations in the spatial domain, and rarely pay attention to the fourier frequency information. In this paper, we develop a novel UIE framework based on spatial-frequency interaction and gradient maps, namely SFGNet, which consists of two stages. Specifically, in the first stage, we propose a dense spatial-frequency fusion network (DSFFNet), mainly including our designed dense fourier fusion block and dense spatial fusion block, achieving sufficient spatial-frequency interaction by cross connections between these two blocks. In the second stage, we propose a gradient-aware corrector (GAC) to further enhance perceptual details and geometric structures of images by gradient map. Experimental results on two real-world underwater image datasets show that our approach can successfully enhance underwater images, and achieves competitive performance in visual quality improvement. The code is available at https://github.com/zhihefang/SFGNet.

Cell-type annotation is a critical step in single-cell data analysis. With the development of numerous cell annotation methods, it is necessary to evaluate these methods to help researchers use them effectively. Reference datasets are essential for evaluation, but currently, the cell labels of reference datasets mainly come from computational methods, which may have computational biases and may not reflect the actual cell-type outcomes. This study first constructed an experimentally labeled immune cell-subtype single-cell dataset of the same batch and systematically evaluated 18 cell annotation methods. We assessed those methods under five scenarios, including intra-dataset validation, immune cell-subtype validation, unsupervised clustering, inter-dataset annotation, and unknown cell-type prediction. Accuracy and ARI were evaluation metrics. The results showed that SVM, scBERT, and scDeepSort were the best-performing supervised methods. Seurat was the best-performing unsupervised clustering method, but it couldn't fully fit the actual cell-type distribution. Our results indicated that experimentally labeled immune cell-subtype datasets revealed the deficiencies of unsupervised clustering methods and provided new dataset support for supervised methods.

Compared to satellite imagery, ground-based cameras capture cloud data (ground-to-sky data) with higher temporal and spatial resolutions, providing more detailed cloud information. However, the spectral information available in ground-to-sky data is limited. Therefore, extracting features with strong discrimination from optical remote sensing images (ORSIs) is challenging. Currently, deep learning-based cloud detection methods face two main challenges. Firstly, although Convolutional Neural Networks (CNNs) effectively extract high-frequency (HF) components from images through convolutions, they struggle to capture low-frequency (LF) components, which are capable of representing global features and target structures. Secondly, in ORSIs, the spectral characteristics of thin clouds and the sky are similar, making it difficult to distinguish cloud regions from the background. To address these challenges, we propose a network consisting of two main modules: the Mixer Module (MM) and the Cloud Aware Attention Module (CAAM). The MM comprises a HF and a LF components extraction branch. The HF branch extracts local textures through max-pooling and parallel convolution operations. The LF branch captures long-range dependency by decomposing a large kernel convolution. It leverages the advantages of both convolution and self-attention to effectively capture global features. In addition, we introduce the CAAM, which quantifies images into histograms to separate clouds from the background and enhances the perception of clouds using attention mechanism. We conducted experiments using both daytime and nighttime cloud image data from the SWINySeg dataset with mIoU reaching 88.93% and OA reaching 93.97%. The results demonstrate that our proposed method achieves promising performance compared to state-of-the-art cloud detection methods.

In this paper, we propose an end-to-end keyword spotting scheme that applies deep learning techniques in a homomorphic encryption domain. Leveraging the complex number encryption capability of homomorphic encryption algorithms, we introduce a complex neural network and design every modules of it, to accommodate the properties and characteristics of homomorphic encryption. Considering the relatively slow computation speed of homomorphic encryption, we employ convolution decomposition to reduce the cost of computational of the network, effectively minimizing computational time while preserving network performance. We have tested our proposed method and found that it significantly accelerates computation speed with minimal performance sacrifice when compared to the current state-of-the-art methods, thereby better balancing network performance and efficiency.

It is desirable to develop efficient image rescaling methods to transmit digital images with different resolutions between devices and assure visual quality. In image downscaling, the inevitable loss of high-frequency information makes the reverse upscaling highly ill-posed. Recent approaches focus on joint learning of image downscaling and upscaling (e.g., rescaling). However, existing methods still fail to recover satisfactory high-frequency signals when upscaling. To solve it, we propose high-frequency flow (HfFlow), which learns the distribution of high-frequency signals during rescaling. HfFlow is an overall invertible framework with a conditional flow on the high-frequency space to compensate for the information lost during downscaling. To facilitate finding the optimal upscaling solution, we introduce a reference low-resolution (LR) manifold and propose a cross-entropy Gaussian loss (CGloss) to force the downscaled manifold closer to the reference LR manifold and simultaneously fulfill recovering missing details. HfFlow can be generalized to other scale transformation tasks such as image colorization with its excellent rescaling capacity. Qualitative and quantitative experimental evaluations demonstrate that HfFlow restores rich high-frequency details and outperforms state-of-the-art rescaling methods in PSNR, SSIM, and perceptual quality metrics.

As individuals and companies become more aware of copyright, image watermarking technology is being used more and more in reality. Due to the limitation of local storage and computing power and privacy protection concerns, privacy-preserving watermarking techniques based on cloud computing are gradually gaining more and more attention. We propose an encrypted image watermarking scheme based on discrete cosine transform (DCT) under cloud computing. Using homomorphic encryption (HE), we achieve secure embedding of watermarks on images in the homomorphic encryption domain. The cloud server accomplishes the watermark embedding algorithm. During the embedding process, the cloud server does not need to decrypt the encrypted image, thus ensuring the security of the original image data. We theoretically analyze the security and computational complexity of the whole scheme. Our experimental results show the effectiveness and reliability of our watermarking scheme.

People encounter a variety of continuous decision-making (DM) problems in the real world. Reinforcement learning (RL) is a promising technique to solve these problems. This paper proposes a privacy-preserving Q-learning decision-making scheme (PQDM). Based on distributed homomorphic encryption (HE), we design several secure protocols to implement the under-lying nonlinear operations such as comparing, maximizing, and maximizing parameter solving. Based on the designed security protocols, we propose a secure decision-making protocol in cloud computing, which enables the cloud server to perform element selection and Q-learning functions on ciphertext data. During the entire process, the cloud server does not need to know the actual state, thus guaranteeing the security of the original state information. We analyze the security and complexity of the whole scheme theoretically. Our experimental results show our proposed scheme’s effectiveness and good spatio-temporal performance.

Service robots have developed considerably in recent years. This study aims to propose two approaches for controlling the motion of a service robot as it pours liquid precisely from an unknown container into another unknown container without the need of any external tools. To realize this task, we must resolve two sub-tasks, which are determining the poured volume and controlling the pouring action. Our first proposal concentrates on the target container. The poured volume is calculated using a model of target container and the height of liquid in the target container. The action is controlled using a proportional-derivative controller, which considers the angular speed of the pouring container as a process variable and the poured volume as a control variable. Our second method concentrates on the pouring container. The poured volume is calculated using the relation between the angle of the pouring container and poured volume. The action is controlled with a simple proportional controller that takes the angular speed of the pouring container as a process variable and target angle as a control variable. A point cloud is used to model the two containers. These two methods were implemented in a dualarm robot system for testing, and the results show that both methods are effective for controlling precise pouring tasks.