Learning-based Underwater Image Enhancement (UIE) methods have made significant progress. Limited by the manual label selection process, the limited quantity and outdated label quality of UIE datasets have severely hindered the development of UIE society. The urgent demand for more and better paired training samples motivates us to propose Ensemble-Select (E-Select), a strategy that can serve as an alternative to fully manual annotation and can enable continuous expansion of dataset size and optimization of label quality. However, expanding size will encounter new images, optimizing labels will encounter new algorithms and the proposed strategy is required to maintain strong generalization in both scenarios, which overwhelms many IQA methods. This work improves generalization in two ways. First, three primary influencing factors and their interrelationships in quality assessment are systematically analyzed. Specifically, we first explore the interactive relationship between content and distortion perception, and further investigate the guiding value of aesthetic-aware features in image quality perception. Then, the proposed Distortion-Content Interaction Module (DCIM) enables the network to focus on perceptually important distortion features guided by content. Second, we investigate a multi-perspective quality evaluation framework based on the ensemble learning paradigm. Building upon the availability of numerous outstanding IQA works, we initially demonstrate their distinct excel regions and evaluation biases. Subsequently, we explore the ensemble of their results through the proposed Aesthetic-Guided Quality Regression module (AGQR), which generates dynamic quality regression layers and derives image-specific quality perception rules based on aesthetic features. We then construct the first expandable, updatable UIE dataset with the help of E-Select. We collect over 50k real underwater image pairs with optimal labels, covering diverse scenes and varied degradation characteristics. Unlike other datasets, our dataset can consistently expand the number of paired samples and maintain optimal labeling without requiring extensive human labor. Experiments show the facilitating effect of the newly constructed dataset on UIE and the SOTA performance of E-Select. Codes and datasets are available at URL.

Underwater object detection (UOD) plays an important role in the exploitation of marine ecological resources. Different from terrestrial images, the complex underwater environment leads to significant degradation in underwater images, which brings great difficulty in accurate object detection. In recent years, many specially designed UOD methods have been proposed to improve the detection precision in two aspects based on underwater image characteristics: 1) Some UOD methods utilize underwater image enhancement (UIE) to alleviate degradation with the expectation of clean features. However, neither preprocessing nor cascade approaches are fully effective for detection-oriented enhancement, while the additional UIE network increases inference time. 2) Other UOD methods consider low visibility of objects, blurriness of small objects, and occlusion problems. However, the semantic complementarity between objects of the same category but different qualities and the background patterns of specific objects are ignored. Based on these two observations, we propose a novel framework for the UOD task, which performs feature enhancement in two ways. First, a group contrastive-based feature enhancement module (GCFEM) is proposed to bridge UIE and UOD. Specifically, multiple enhanced versions by UIEs are evaluated by the object detection precision evaluation pipeline. Then, group-based contrastive learning is introduced, which utilizes multiple groups of enhanced versions to guide the backbone in extracting detection-friendly features. Second, a prior-guided dual-reference feature enhancement module (PDFEM) is proposed to enhance the representation of objects further. Specifically, the explicit object-object relationship allows low-quality object regions to refer to high-quality ones, guided by a transmission map. At the same time, the implicit object-background relationship provides cues about the surroundings for the representation of the objects. Experimental results demonstrate that the proposed algorithm outperforms many state-of-the-art UOD methods on RUOD and URPC2020 datasets.

Due to the complex underwater imaging process, underwater images contain a variety of unique distortions. While existing underwater image quality assessment (UIQA) methods have made progress by highlighting these distortions, they overlook the fact that image content also affects how distortions are perceived, as different content exhibits varying sensitivities to different types of distortions. Both the characteristics of the content itself and the properties of the distortions determine the quality of underwater images. Additionally, the intertwined nature of content and distortion features in underwater images complicates the accurate extraction of both. In this paper, we address these issues by comprehensively accounting for both content and distortion information and explicitly disentangling underwater image features into content and distortion components. To achieve this, we introduce a dynamic content-distortion guiding and feature disentanglement network (DysenNet), composed of three main components: the feature disentanglement sub-network (FDN), the dynamic content guidance module (DCM), and the dynamic distortion guidance module (DDM). Specifically, the FDN disentangles underwater features into content and distortion elements, allowing us to more clearly measure their respective contributions to image quality. The DCM generates dynamic multi-scale convolutional kernels tailored to the unique content of each image, enabling content-adaptive feature extraction for quality perception. The DDM, on the other hand, addresses both global and local underwater distortions by identifying distortion cues from both channel and spatial perspectives, focusing on regions and channels with severe degradation. Extensive experiments on UIQA datasets demonstrate the state-of-the-art performance of the proposed method.

Single image superresolution (SISR) is a critical task in computer vision with significant applications in both natural and industrial contexts. Although transformer-based approaches for SISR have achieved notable progress due to their exceptional representational capabilities, their quadratic computational complexity poses challenges for deployment on devices with limited resources. Conversely, convolutional networks (ConvNets) are inherently efficient but have difficulty capturing long-range pixel relationships because of their focus on spatial locality. This gives rise to a complementary relationship between the representational power of transformers and the efficiency of ConvNets, both of which are essential for practical applications. Motivated by this, in this article, we introduce TSCN, a novel transformer-style ConvNet. Our analysis highlights the strengths of transformers, including large-range dependencies modeling, two-order features interaction, input self-adaptation, and incorporating advanced components. Based on these insights, we guide the design of ConvNets to fully exploit these characteristics. Specifically, we rethink spatial convolution to enhance the modeling of spatial features and modify the macrostructure of the transformer by replacing self-attention and feed-forward network with the large-range multiorder convolution modulation (LMCM) layer and spatial awareness dynamic feature flow (SADFF) layer. The LMCM integrates reweighting into the large-range convolutional modulation technology, allowing self-adaptive recalibration of input representations using convolutional features as weight matrices and multiorder features interaction. In addition, the SADFF introduces spatial awareness, locality, and dynamic information flow modulation between layers. Experimental results demonstrate that our TSCN outperforms the state-of-the-art method SRFormer on multiple benchmarks by 0.03$\sim$0.17 dB, while using fewer parameters and computations.

The aim of image restoration in the presence of rain and snow effects is to eliminate these disturbances while retaining the underlying background structure. Most existing methods tend to directly learn the mapping from corrupted images to clean ones, often resulting in residual rain or snow artifacts and compromised background structures. In this work, both theoretical analysis and experimental findings confirm the robustness of the hue channel in HSV color space to rain and snow disturbances, even when extracted from corrupted images. Motivated by this insight, we propose to leverage the global clean background cues inherent in the hue channel to guide the network in preserving the image background structure and removing interference. To this end, we introduce the global background prior-guided network (GBPG-Net) for restoring rain and snow-affected images, which employs a triangular formation to facilitate continuous interaction and updating of the global background prior (GBP) with the image feature within the GBPG-unit, resulting in improved interference removal and background structure preservation. Specifically, the GBPG-Net incorporates the global clean background prior injector (GCBPI) to inject the GBP into the network. Subsequently, the prior-guided local detail excavation (PGLDE) module, built on GCBPI, further refines interference removal and structure preservation to process local details intricately. Finally, the prior-guided local-global aggregation (PGLGA) module aggregates global background features with local detailed features, enabling the network to better understand the overall content and subtle interference for more accurate reconstruction. Quantitative and qualitative evaluations on synthetic and real datasets demonstrate the effectiveness of the proposed GBPG-Net in deraining and desnowing tasks, highlighting its advantages over existing methods. The code and supplementary documentation are available at https://github.com/liux520/GBPG-Net.

Secure persistent memory (PM) systems have to guarantee the crash consistency for secure data, which requires atomically persisting both data and its security metadata (e.g., the counter and Merkle tree nodes). Existing work introduces persistent registers and uses the redo-style method to ensure the atomic durability of the data and security metadata for a single-core system. Unfortunately, their performance decreases sharply in multi-core secure PM systems because the atomic durability guarantee procedure for each write request is serialized.In this work, we propose PireSPM, a pipeline-based and recoverable scheme for secure persistent memory for multi-core architecture. PireSPM supports processing multiple atomic durability requests at a time without incurring recoverability problems to the secure PM. We further optimize PireSPM by combining Merkle tree updates and reducing the latency incurred by the atomic durability guarantee without breaking the system’s recoverability. Experimental results show that PireSPM with all these optimizations achieve up to 1.49× speedup and 4.97× speedup respectively over the state-of-the-art method in 1-core and 16-core systems.

Clear and high-resolution (HR) underwater images are indispensable in acquiring underwater information. However, existing underwater image enhancement and super-resolution (UIESR) networks achieve limited enhancement-super-resolution performance on real-world turbid low-resolution (LR) underwater images because (1) they assume that the resolution degradation is simple and known bicubic down-sampling, generating unrealistic training data for UIESR task; (2) they extract known priors from the underwater imaging model, which is meager to address complex UIESR problems caused by unknown mixed dual-degradation; and (3) they ignore the interaction between blurring and color casts in the RGB color space, leading to unsatisfactory correction results of two distortions. To address these issues, we propose a realistic UIESR network (RUIESR) consisting of three parts: a realistic LR image generation module (RLGM), a dual-degradation estimation module (DEM), and an enhancement and super-resolution module (ESRM). Firstly, RLGM aims to generate LR images obeying underwater LR image distribution by learning real LR properties from unpaired real LR-HR underwater images for training. Secondly, a contrast-driven learning strategy is proposed in the DEM to accurately estimate unknown dual-degradation priors that can aid the reconstruction task. Finally, ESRM is proposed to enhance textures and correct color casts, which includes a dual-branch structure to separate blurring and color casts distortions and utilizes specific priors for each distortion to assist reconstruction. Extensive experiments on real and synthetic underwater datasets show that the proposed RUIESR outperforms existing works regarding visual quality and quantitative metrics.

Underwater images are often affected by color cast and blurring, which degrade the performance of underwater machine vision tasks. While existing underwater image enhancement (UIE) methods have been proposed to improve image quality for human perception, their effectiveness in enhancing machine vision performance is limited. In this article, a novel unsupervised UIE framework based on disentangled representation (DR) is proposed, which is designed for machine vision tasks. Specifically, the proposed framework disentangles the underwater image into two parts in the latent space according to whether they are beneficial to machine vision tasks: the task-friendly content features and the task-unfriendly distortion features. In addition, a semantic-aware contrastive module (SACM) is employed to alleviate the impact of losing key information required for machine vision tasks using the strategy of contrastive learning. Furthermore, two branches on the features and images are incorporated into the enhancement network, which serve the purpose of delivering task-relevant information to the enhancement model and guide the network to generate task-friendly images. Evaluation of the proposed method is conducted on multiple underwater image datasets, and a comparison is made with state-of-the-art enhancement methods in terms of machine vision performance. The experimental results demonstrate that the proposed method surpasses existing approaches in improving the accuracy and robustness of machine vision tasks, including object detection, semantic segmentation, and saliency detection in underwater environments. Our code is available athttps://github.com/gemyumeng/TFUIE.

Underwater image enhancement (UIE) is a meaningful but challenging task, and many learning-based UIE methods have been proposed in recent years. Although much progress has been made, these methods still have two issues: (1) There exists a significant region-wise quality difference in a single underwater image due to the underwater imaging process, especially in regions with different scene depths. However, existing methods neglect this internal characteristic of underwater images, resulting in inferior performance; (2) Due to the uniqueness of the acquisition approach, underwater image acquisition tools usually capture multiple images in the same or similar scenes. Thus, the underwater images to be enhanced in practical usage are highly correlated. However, when processing a single image, existing methods do not consider the rich external information provided by the related images. There is still room for improvement in their performance. Motivated by these two aspects, we propose a novel internal-external representation learning (UIERL) network to better perform UIE tasks with internal and external information, simultaneously. In the internal representation learning stage, a new depth-based region feature guidance network is designed, including a region segmentation module based on scene depth to sense regions with different quality levels, followed by a region-wise space encoder module. With performing region-wise feature learning for regions with different quality separately, the network provides an effective guidance for global features and thus guides intra-image differentiated enhancement. In the external representation learning stage, we first propose an external information extraction network to mine the rich external information in the related images. Then, internal and external features interact with each other via the proposed external-assist-internal module (external features are updated with the help of internal features) and internal-assist-external module (internal features are updated with the help of external features). In this way, our UIERL fully explores the rich internal and external information to better enhance a single image. All results show that our method can achieve state-of-the-art performance on five benchmarks.

Background (BG)tasks are maintained indispensably in multiple layers of storage systems, from applications to flash-based SSDs. They launch a large amount of I/Os, causing significant interference withforeground (FG)I/O performance. Our key insight is that, to mitigate such interference, holistic scheduling of system-wide, multi-source BG I/Os is required and can only be realized at the underlying SSD layer. Only the SSD has a global view of all FG and BG I/Os as well as direct information and control about flash storage resources. We are thus inspired to propose a novel I/O scheduling architecture, calledHuFu. It provides a framework for host software to register BG tasks and offload their I/O scheduling into the SSD. Then, the SSD-internal I/O scheduler prioritizes FG I/O processing, while BG I/Os are scheduled opportunistically by utilizing flash parallelism and idleness. To verifyHuFu, we perform case studies on RocksDB and compares it with several state-of-the-art host-side I/O scheduling schemes. Experimental results show thatHuFucan significantly alleviate performance interference caused by BG I/Os and improve SSD bandwidth utilization, thus improving the FG throughput, average and tail latencies (e.g., by about 18% in a write-heavy workload).

Machine analysis of underwater images is essential to most underwater applications. However, both the limitation of communication bandwidth and underwater degradation bring much difficulty to accurate machine recognition at the end system. Few existing underwater compression methods consider unique underwater prior knowledge to better serve for machine vision under low bit-rates. To address this problem, we propose a novel underwater image compression framework for machines, which utilizes underwater priors to contrastively enhance degraded features by contrastive learning and efficiently compress machine-friendly features under low bit-rates. A dataset is built to provide positive and negative samples for contrastive learning based on machine analysis performance. At the encoder side, a feature extractor and a feature encoder are employed to extract machine-related features and compress them into compact representations. To alleviate the effect of underwater degradation on machine vision, a prior-guided contrastive feature enhancement module is proposed to learn more machine-friendly features based on positive and negative samples from our dataset. Then a feature refinement block is designed to remove channel-wise redundancy and focus spatial-wise importance based on high similarity of machine-related features and characteristics of underwater images. More compact representations are obtained without degrading analysis performance under low bit-rates. At the decoder side, both machine-friendly features and image are reconstructed to support different types of analysis tasks. Experimental results demonstrate the superiority of our framework in machine vision tasks compared with traditional compression methods and learned-based methods. Besides, our method still preserves basic capability of human perception.

Since underwater images are seriously degraded due to the attenuation of light, artificial light (AL) is often used to assist photography in underwater. However, the normal underwater imaging process is changed by the AL. It is observed that the AL source typically alters the light condition to a large extent, resulting in non-uniform illumination of images. In addition, the color distortion of the area affected by AL is little because the AL close to the object suffers little attenuation. However, most existing underwater image enhancement algorithms ignore this phenomenon. In their results, the areas affected by AL tend to be over-enhanced or over-exposed and even affect the overall enhancement effect. To this end, we propose a novel underwater image enhancement algorithm (UIALN) based on luminance correction and AL area color self-guided restoration. The underwater image is converted into the LAB color space, where the AL and pseudo-blur effect on the L channel are removed based on the luminance correction network, and the color casts on the AB channels is removed by the guidance of the AL area. Specifically, a luminance correction network is first designed based on the retinex decomposition to correct luminance, where the uneven luminance caused by AL is corrected in the illumination layer decomposed by the L channel because AL is usually white light. After that, the AL area is detected by the difference between before and after luminance correction. Second, an AL area self-guidance network is designed to assist the restoration of the color channels AB. The color restoration module utilizes the internal characteristics of the image, where the characteristics of the AL area are utilized as the prior to make the color easy to be restored. In addition, to facilitate the training and testing of the algorithm, a method of synthetic underwater images with AL is proposed based on underwater image imaging model, and a new underwater image dataset with artificial light (UIDWAL) is provided. Experimental results show that our UIALN outperforms the existing state-of-the-art approaches for the enhancement of both synthetic and real underwater images with AL.

Underwater enhanced images (UEIs) are affected by not only the color cast and haze effect due to light attenuation and scattering, but also the over-enhancement and texture distortion caused by enhancement algorithms. However, existing underwater image quality assessment (UIQA) methods mainly focus on the inherent distortion caused by underwater optical imaging, and ignore the widespread artificial distortion, which leads to poor performance in evaluating UEIs. In this paper, a novel mapping-based underwater image quality representation is proposed. We divide underwater enhanced images into different domains and utilize a feature vector to measure the distance from the raw image domain to each enhanced image domain. The length and direction of the vector are defined as the enhancement degree and enhancement direction of the image. We construct a best enhancement direction and map other vectors to this direction to obtain the corresponding quality representation. Based on this, a novel network, called generation-based joint luminance-chrominance underwater image quality evaluation (GLCQE), is proposed, which is mainly divided into three parts: bi-directional reference generation module (BRGM), chromatic distortion evaluation network (CDEN), and sharpness distortion evaluation network (SDEN). BRGM is designed to generate two reference images about the unenhanced and the optimal enhanced versions of input UEI. In addition, the distortions in the luminance and chrominance domains of the UEI are analyzed. The luminance and chrominance channels of images are separated and input to SDEN and CDEN respectively to detect different distortions. A multi-scale feature mapping module is proposed in CDEN and SDEN to extract the feature representation of quality in chrominance and luminance of these images respectively. Moreover, a parallel spatial attention module is designed to focus on distortions in structural space by utilizing the different receptive fields of the convolution layer, due to the diverse manifestations of structural loss in the image. Finally, the mapped features extracted by two collaborative networks help the model evaluate the quality of underwater images more accurately. Extensive experiments demonstrate the superiority of our model against other representative state-of-the-art models.

Image acquisition and reconstruction play an important role in underwater detections and explorations. However, the limited underwater acoustic communication channels and narrow bandwidth resources will have a great impact on the performance of the traditional data acquisition methods, resulting in the loss of details and blur in reconstructed underwater images. Compressive sensing theory (CS) which can reconstruct images from fewer measurement than that required by Nyquist sampling law has been proved to have good effect on image sampling and reconstruction. Nevertheless, the existing CS methods are not suitable for underwater images because most of them are designed for on-land images which have huge differences from underwater images. In this paper, we propose a novel priors guided adaptive underwater compressive sensing framework, dubbed UCSNet, which can effectively sample and reconstruct underwater images under a fixed low sampling ratio. In particular, our framework is composed of three sub-networks: underwater priors extraction and guidance network (UEGN), sampling matrix generation network (SMGNet) and channel-wise reconstruction network (CWRNet). Specifically, inspired by the underwater imaging physical models, UEGN is designed to extract features of underwater priors information and combine them adaptively. UEGN also introduce the imaging process into CS task to make sampling and reconstruction consistent with underwater imaging characteristics. SMGNet uses underwater content degradation to assist the analysis of structural information to generate sampling matrices. Considering the monotony of color tones caused by light absorption in underwater images, CWRNet embedded with the channel-wise module (CWM) is designed to enforce the whole network to allocate different number of sampling points on luminance and chrominance channels respectively and make feature maps extracted from them complement with each other. Experimental results demonstrate that our proposed framework can achieve both PSNR and SSIM gains on underwater images reconstruction quality and have greater visual quality than other state-of-art methods under fixed sampling ratios.

Forward-looking sonar (FLS) imagery system plays a significant role in oceanic object recognition and detection since it can overcome the limitation of lighting conditions and reflect the real situation of the underwater environment. However, object detection algorithms for FLS images remain challenging for two main reasons: 1) the noise caused by the coherent characteristic of the scattering phenomenon impairs the detector capture of target information and 2) the scene prior based on the uneven target scale distribution is generally neglected, which leads to the detector generating redundant anchors and slows down detection efficiency. Confronting such challenges, this article characterizes the noise and the uneven target scale distribution in FLS images as multiplicative speckle noise and scene prior, respectively. Therefore, we propose a novel underwater FLS image detection network, namely UFIDNet, to further improve detection performance by considering speckle noise reduction and scene prior in FLS images. More specifically, a speckle reduction auxiliary branch (SRAB) is designed to introduce additional despeckled supervision information to encourage the feature extractor to produce clean features and share them with the detection pipeline during the training phase. In particular, the noise distribution of FLS images is excavated for synthetic dataset construction and despeckle network (DSN) design to obtain despeckled supervision images. In addition, a feature selection strategy (FSS) embedded in detection branch is designed to screen out feature levels that do not match the target size, thus significantly reducing the generation of redundant anchors and improving detection speed. Experimental results show that our UFIDNet achieves 70.5% and 47.3% average precision (AP), 81.3% and 54.6% average recall (AR) ($\text {AR}_{\text {max=10}}$), 27.0 and 26.1 FPS on two real FLS datasets, respectively, outperforming many state-of-the-art general detectors and sonar image detectors.

Recently, learning-based algorithms have shown impressive performance in underwater image enhancement. Most of them resort to training on synthetic data and obtain outstanding performance. However, these deep methods ignore the significant domain gap between the synthetic and real data (i.e., inter-domain gap), and thus the models trained on synthetic data often fail to generalize well to real-world underwater scenarios. Moreover, the complex and changeable underwater environment also causes a great distribution gap among the real data itself (i.e., intra-domain gap). However, almost no research focuses on this problem and thus their techniques often produce visually unpleasing artifacts and color distortions on various real images. Motivated by these observations, we propose a novel Two-phase Underwater Domain Adaptation network (TUDA) to simultaneously minimize the inter-domain and intra-domain gap. Concretely, in the first phase, a new triple-alignment network is designed, including a translation part for enhancing realism of input images, followed by a task-oriented enhancement part. With performing image-level, feature-level and output-level adaptation in these two parts through jointly adversarial learning, the network can better build invariance across domains and thus bridging the inter-domain gap. In the second phase, an easy-hard classification of real data according to the assessed quality of enhanced images is performed, in which a new rank-based underwater quality assessment method is embedded. By leveraging implicit quality information learned from rankings, this method can more accurately assess the perceptual quality of enhanced images. Using pseudo labels from the easy part, an easy-hard adaptation technique is then conducted to effectively decrease the intra-domain gap between easy and hard samples. Extensive experimental results demonstrate that the proposed TUDA is significantly superior to existing works in terms of both visual quality and quantitative metrics.

QLC flash-based SSDs are gaining increasing attention and are expected to be widely used in read-intensive application scenarios, since they provide high density and low cost but suffer from poor write endurance and performance. QLC flash has four types of pages, between which read latency variation is as large as 1.6 to 4.8 times. This raises a critical concern for QLC SSDs to provide adequate and stable read performance. Notice that the SSD-internal cache (built with DRAM or non-volatile RAM) has long been utilized to improve write performance and lifetime. In this paper, we argue that the cache also plays an important role in read performance optimization of QLC SSDs. We design a novel flash page type aware read cache scheme, called PACA. It exploits read latency variation of QLC pages to prioritize caching data stored in high-latency QLC pages in a workload-adaptive manner. We verified PACA in FEMU, a popular SSD emulator. Experimental results show that PACA can reduce the average SSD read latency by up to 44.5%, compared with a baseline read cache scheme being unaware of flash page types.

Underwater images suffer from severe color casts, low contrast, and blurriness, which greatly degrade the visibility and color fidelity of underwater images. Recently, numerous underwater image enhancement (UIE) algorithms have been proposed. Existing synthetic datasets-based deep learning methods employ synthetic datasets to train UIE models. However, there is a gap between synthetic datasets and real underwater images, leading to poor generalization of synthetic datasets-based UIE methods. Besides, existing real datasets-based deep learning methods largely focus on minimizing the mean squared reconstruction error between UIE results and corresponding ground-truth on the real datasets, but do not take human visual perception into account. Thus, although they achieve high PSNR between UIE results and corresponding ground-truth obtained by user study on the real datasets, they often achieve unsatisfactory perceptual quality. To address these problems, we propose a Human Perceptual Quality Driven Underwater Image Enhancement Framework (HPQ-UIEF) to achieve better results in human perceptual quality and maintain satisfactory PSNR, which is trained on a real underwater enhancement quality assessment database (UEQAB). Specifically, an Underwater Image Quality Assessment Network (UIQAN) for UIE images is first proposed to assist UIE task, in which a novel depth map prior spatial attention block (DPPAB) is embedded into UIQAN. The DPPAB can adaptively recalibrate the quality-aware feature maps and model human visual attention in a data-driven manner. Then, the UIE model is proposed, in which the UIQAN is introduced as the loss function to optimize our UIE model in the direction of perceptual metrics. Moreover, since the confidence map acquired by UIQAN can effectively reflect the sensitivity of human perceptual of local area in an UIE image, the confidence map is introduced to our UIEF to help our UIEF to perceive the perceptually important regions. Thus, the confidence map is down-sampled and then concatenated into the decoder module of the UIE model, which can further improve the perceptual quality of the UIE results. Extensive experimental results show that the proposed HPQ-UIEF outperforms state-of-the-art UIE methods qualitatively and quantitatively.

Underwater images suffer from severe color casts, low contrast and blurriness, which are caused by scattering and absorption when light propagates through water. However, existing deep learning methods treat the restoration process as a whole and do not fully consider the underwater physical distortion process. Thus, they cannot adequately tackle both absorption and scattering, leading to poor restoration results. To address this problem, we propose a novel two-stage network for underwater image restoration (UIR), which divides the restoration process into two parts viz. horizontal and vertical distortion restoration. In the first stage, a model-based network is proposed to handle horizontal distortion by directly embedding the underwater physical model into the network. The attenuation coefficient, as a feature representation in characterizing water type information, is first estimated to guide the accurate estimation of the parameters in the physical model. For the second stage, to tackle vertical distortion and reconstruct the clear underwater image, we put forth a novel attenuation coefficient prior attention block (ACPAB) to adaptively recalibrate the RGB channel-wise feature maps of the image suffering from the vertical distortion. Experiments on both synthetic dataset and real-world underwater images demonstrate that our method can effectively tackle scattering and absorption compared with several state-of-the-art methods.