The efficacy and safety of acupuncture in the treatment of sudden sensorineural hearing loss: A systematic review and meta-analysis.

Background: Despite numerous pharmacological and therapeutic options for sudden sensorineural hearing loss(SSNHL), treatment remains challenging due to various side effects and suboptimal efficacy. Acupuncture, as a complementary and alternative therapy, has achieved some success in this field. The objective of this study was to systematically evaluate the efficacy and safety of acupuncture for SSNHL, and to seek robust evidence-based medical evidence for the therapeutic effect of acupuncture.

INformer: Inertial-Based Fusion Transformer for Camera Shake Deblurring.

Inertial measurement units (IMU) in the capturing device can record the motion information of the device, with gyroscopes measuring angular velocity and accelerometers measuring acceleration. However, conventional deblurring methods seldom incorporate IMU data, and existing approaches that utilize IMU information often face challenges in fully leveraging this valuable data, resulting in noise issues from the sensors. To address these issues, in this paper, we propose a multi-stage deblurring network named INformer, which combines inertial information with the Transformer architecture.

Adaptive Blind Super-Resolution Network for Spatial-Specific and Spatial-Agnostic Degradations.

Prior methodologies have disregarded the diversities among distinct degradation types during image reconstruction, employing a uniform network model to handle multiple deteriorations. Nevertheless, we discover that prevalent degradation modalities, including sampling, blurring, and noise, can be roughly categorized into two classes. We classify the first class as spatial-agnostic dominant degradations, less affected by regional changes in image space, such as downsampling and noise degradation.

Revitalizing Convolutional Network for Image Restoration.

Image restoration aims to reconstruct a high-quality image from its corrupted version, playing essential roles in many scenarios. Recent years have witnessed a paradigm shift in image restoration from convolutional neural networks (CNNs) to Transformer-based models due to their powerful ability to model long-range pixel interactions. In this paper, we explore the potential of CNNs for image restoration and show that the proposed simple convolutional network architecture, termed ConvIR, can perform on par with or better than the Transformer counterparts.

Application of chiral recyclable catalysts in asymmetric catalysis.

Chiral drugs hold a significant position within the contemporary pharmaceutical market, and the chiral catalysts play a crucial role in their synthesis. However, current chiral catalysts encounter challenges pertaining to their separation from products and the recycling process. The utilization of chiral recyclable catalysts not only reduces production costs but also aligns with the growing emphasis on environmentally-friendly chiral synthetic chemistry.

Correcting Optical Aberration via Depth-Aware Point Spread Functions.

Optical aberration is a ubiquitous degeneration in realistic lens-based imaging systems. Optical aberrations are caused by the differences in the optical path length when light travels through different regions of the camera lens with different incident angles. The blur and chromatic aberrations manifest significant discrepancies when the optical system changes.

Cylin-Painting: Seamless 360° Panoramic Image Outpainting and Beyond.

Image outpainting gains increasing attention since it can generate the complete scene from a partial view, providing a valuable solution to construct 360° panoramic images. As image outpainting suffers from the intrinsic issue of unidirectional completion flow, previous methods convert the original problem into inpainting, which allows a bidirectional flow. However, we find that inpainting has its own limitations and is inferior to outpainting in certain situations.

Multi-Scale Fusion and Decomposition Network for Single Image Deraining.

Convolutional neural networks (CNNs) and self-attention (SA) have demonstrated remarkable success in low-level vision tasks, such as image super-resolution, deraining, and dehazing. The former excels in acquiring local connections with translation equivariance, while the latter is better at capturing long-range dependencies. However, both CNNs and Transformers suffer from individual limitations, such as limited receptive field and weak diversity representation of CNNs during low efficiency and weak local relation learning of SA.

PFONet: A Progressive Feedback Optimization Network for Lightweight Single Image Dehazing.

Image dehazing is an effective means to enhance the quality of images captured in foggy or hazy weather conditions. However, existing image dehazing methods are either ineffective in dealing with complex haze scenes, or incurring too much computation. To overcome these deficiencies, we propose a progressive feedback optimization network (PFONet) which is lightweight yet effective for image dehazing.

Image Restoration via Frequency Selection.

Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart. Besides dealing with this long-standing task in the spatial domain, a few approaches seek solutions in the frequency domain by considering the large discrepancy between spectra of sharp/degraded image pairs. However, these algorithms commonly utilize transformation tools, e.

Multi-scale, multi-dimensional binocular endoscopic image depth estimation network.

During invasive surgery, the use of deep learning techniques to acquire depth information from lesion sites in real-time is hindered by the lack of endoscopic environmental datasets. This work aims to develop a high-accuracy three-dimensional (3D) simulation model for generating image datasets and acquiring depth information in real-time. Here, we proposed an end-to-end multi-scale supervisory depth estimation network (MMDENet) model for the depth estimation of pairs of binocular images.

Event-Aware Video Deraining via Multi-Patch Progressive Learning.

In this paper, we address the problem of video-based rain streak removal by developing an event-aware multi-patch progressive neural network. Rain streaks in video exhibit correlations in both temporal and spatial dimensions. Existing methods have difficulties in modeling the characteristics.

Multi-Exposure Image Fusion via Deformable Self-Attention.

Most multi-exposure image fusion (MEF) methods perform unidirectional alignment within limited and local regions, which ignore the effects of augmented locations and preserve deficient global features. In this work, we propose a multi-scale bidirectional alignment network via deformable self-attention to perform adaptive image fusion. The proposed network exploits differently exposed images and aligns them to the normal exposure in varying degrees.

Attention-Guided Collaborative Counting.

Existing crowd counting designs usually exploit multi-branch structures to address the scale diversity problem. However, branches in these structures work in a competitive rather than collaborative way. In this paper, we focus on promoting collaboration between branches.

A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging.

Hyperspectral imaging enables many versatile applications for its competence in capturing abundant spatial and spectral information, which is crucial for identifying substances. However, the devices for acquiring hyperspectral images are typically expensive and very complicated, hindering the promotion of their application in consumer electronics, such as daily food inspection and point-of-care medical screening, etc. Recently, many computational spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from widely available RGB images.

Under-Display Camera Image Enhancement via Cascaded Curve Estimation.

The new trend of full-screen devices encourages manufacturers to position a camera behind a screen, i.e., the newly-defined Under-Display Camera (UDC).

Gelatin from leather waste to tough biodegradable packaging film: One valuable recycling solution for waste gelatin from leather industry.

Large amount of gelatin can be extracted from the solid waste in leather industry. The advanced application of such gelatin is always desired by the leather industry, but remains challenging. Considering the urgent requirement of biodegradable plastic film, in this study, the gelatin extracted from waste skin scrap in the leather industry was used to fabricate a waste gelatin-based film with a high gelatin content, excellent mechanical performance, and autonomous biodegradability in natural soil.

Enhanced Spatio-Temporal Interaction Learning for Video Deraining: Faster and Better.

Video deraining is an important task in computer vision as the unwanted rain hampers the visibility of videos and deteriorates the robustness of most outdoor vision systems. Despite the significant success which has been achieved for video deraining recently, two major challenges remain: 1) how to exploit the vast information among successive frames to extract powerful spatio-temporal features across both the spatial and temporal domains, and 2) how to restore high-quality derained videos with a high-speed approach. In this paper, we present a new end-to-end video deraining framework, dubbed Enhanced Spatio-Temporal Interaction Network (ESTINet), which considerably boosts current state-of-the-art video deraining quality and speed.

Video Super-Resolution via a Spatio-Temporal Alignment Network.

Deep convolutional neural network based video super-resolution (SR) models have achieved significant progress in recent years. Existing deep video SR methods usually impose optical flow to wrap the neighboring frames for temporal alignment. However, accurate estimation of optical flow is quite difficult, which tends to produce artifacts in the super-resolved results.

Semantic-Aware Dehazing Network With Adaptive Feature Fusion.

Despite that convolutional neural networks (CNNs) have shown high-quality reconstruction for single image dehazing, recovering natural and realistic dehazed results remains a challenging problem due to semantic confusion in the hazy scene. In this article, we show that it is possible to recover textures faithfully by incorporating semantic prior into dehazing network since objects in haze-free images tend to show certain shapes, textures, and colors. We propose a semantic-aware dehazing network (SDNet) in which the semantic prior is taken as a color constraint for dehazing, benefiting the acquisition of a reasonable scene configuration.

IDRLP: Image Dehazing Using Region Line Prior.

In this work, a novel and ultra-robust single image dehazing method called IDRLP is proposed. It is observed that when an image is divided into n regions, with each region having a similar scene depth, the brightness of both the hazy image and its haze-free correspondence are positively related with the scene depth. Based on this observation, this work determines that the hazy input and its haze-free correspondence exhibit a quasi-linear relationship after performing this region segmentation, which is named as region line prior (RLP).

Face Restoration via Plug-and-Play 3D Facial Priors.

State-of-the-art face restoration methods employ deep convolutional neural networks (CNNs) to learn a mapping between degraded and sharp facial patterns by exploring local appearance knowledge. However, most of these methods do not well exploit facial structures and identity information, and only deal with task-specific face restoration (e.g.

Underwater image restoration via depth map and illumination estimation based on a single image.

For the enhancement process of underwater images taken in various water types, previous methods employ the simple image formation model, thus obtaining poor restoration results. Recently, a revised underwater image formation model (i.e.

Dual Attention-in-Attention Model for Joint Rain Streak and Raindrop Removal.

Rain streaks and raindrops are two natural phenomena, which degrade image capture in different ways. Currently, most existing deep deraining networks take them as two distinct problems and individually address one, and thus cannot deal adequately with both simultaneously. To address this, we propose a Dual Attention-in-Attention Model (DAiAM) which includes two DAMs for removing both rain streaks and raindrops.

Hierarchical Density-Aware Dehazing Network.

The commonly used atmospheric model in image dehazing cannot hold in real cases. Although deep end-to-end networks were presented to solve this problem by disregarding the physical model, the transmission map in the atmospheric model contains significant haze density information, which cannot simply be ignored. In this article, we propose a novel hierarchical density-aware dehazing network, which consists of a the densely connected pyramid encoder, a density generator, and a Laplacian pyramid decoder.