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All-in-one adverse weather removal is an emerging topic on image restoration, which aims to restore multiple weather degradations in a unified model, and the challenges are twofold. First, discover and handle the properties of the multi-domain in the target distribution formed by multiple weather conditions. Second, design efficient and effective operations for different degradations. To resolve this problem, most prior works focus on the multi-domain caused by different weather types. Inspired by inter&intra-domain adaptation literature, we observe that not only weather type but also weather severity introduce multi-domain within each weather type domain, which is ignored by previous methods and further limits their performance. To this end, we propose a degradation type and severity aware model, called UtilityIR, for blind all-in-one bad weather image restoration. To extract weather information from a single image, we propose a novel Marginal Quality Ranking Loss (MQRL) and utilize Contrastive Loss (CL) to guide weather severity and type extraction, and leverage a bag of novel techniques such as Multi-Head Cross Attention (MHCA) and Local-Global Adaptive Instance Normalization (LG-AdaIN) to efficiently restore spatial varying weather degradation. The proposed method can outperform the state-of-the-art methods subjectively and objectively on different weather removal tasks with a large margin, and enjoy fewer model parameters. The proposed method can even restore previously unseen combined multiple degradation images, and modulate restoration level. Implementation code and pre-trained weights will be available at https://github.com/fordevoted/UtilityIR.

In this paper, we proposed the efficient and appealing technique for underwater images enhancement. Underwater images often suffer from haze, color distortion, low contrast and loss of the human acuity due to light scattering and absorption. To tackle these issues, proposed precise model is presented expressively showed that: (1) the estimation of the transmission map in atmospheric scatter model is divided into two cases, which are more applicable to underwater images in different situations, and make the enhanced results more robust; (2) model not only removes haze but also restores lost colors of underwater images in the image de-hazing step instead of color correction. First, we proposed a revised underwater dehazing model aiming to eliminate the color of water directly while solving the problem of haze in the underwater images. Then proposed color correction method can adaptively address the problem of color shifting without any additional information. Furthermore, we design a multi-scale illumination fusion to reveal more details and low illumination parts of the image. Experimental results demonstrate that our proposed method outperforms other methods significantly with $5\% \sim 77\%$ quantitative improvement on all four evaluation performance indices and shows more obvious detailed underwater images. Our method can be applied to underwater detection and exploration as the pre-processing step.

Underwater image suffer from color cast, low contrast and hazy effect, which degraded the high-level vision application. Recent learning-based methods demonstrate astonishing performance on underwater image enhancement, however, most of these works use synthetic pair data for supervised learning and ignore the domain gap to real-world data. Although some work leverage transfer learning and domain adaptation to alleviate this problem, they target to minimize the latent discrepancy of synthesis and real-world data, and make the latent space hard to interpret and cannot manipulate. To solve this problem, we propose a domain adaptation framework for underwater image enhancement via content and style separation, we aim to separate encoded feature into content and style latent and distinguish style latent from different domains, and process domain adaptation and image enhancement in latent space. Our model provide a user interact interface to adjust different enhanced level for continuous change by latent manipulation. Experiment on various public real-world underwater benchmarks demonstrate that the proposed framework is capable to perform domain adaptation for underwater image enhancement and outperform various state-of-the-art underwater image enhancement algorithms in quantity and quality. The model and source code will be available at https://github.com/fordevoted/UIESS.

We proposed a blind image quality assessment model which used classification and prediction for three-dimensional (3D) image quality assessment (denoted as CAP-3DIQA) that can automatically evaluate the quality of stereoscopic images. First, in the classification stage, the model separated the distorted images into several subsets according to the types of image distortions. This process will assign the images with the same distortion type to the same group. After the classification stage, the classified distorted image set is fed into the image quality predictor that contains five different perceptual channels which predict the image quality score individually. Finally, we used the regression module of the support vector machine to evaluate the final image quality score, where the input of the regression model is the combination of five channel’s outputs. The model, we proposed is tested on three public and popular databases, which are LIVE 3D Image Quality Database Phase I, LIVE 3D Image Quality Database Phase II, and MCL 3D Image Quality Database. The experimental results show that our proposed model leads to significant performance improvement on quality prediction for stereoscopic images compared with other existing state-of-the-art quality metrics.

Wireless sensors have been helpful and popular for gathering information, in particular in harsh environments. Due to the limit of computation power and energy, compressive sensing has attracted considerable attention in achieving simultaneous sensing and compression of data on the sensor/encoder side with cheap cost. Nevertheless, along with the increase of the data size, the computation overhead for decoding becomes unaffordable on the user/decoder side. To overcome this problem, by taking advantage of resourceful cloud, it is helpful to leverage the overhead. In this paper, we propose a cloud-assisted compressive sensing-based data gathering system with security assurance. Our system, involving three parties of sensor, cloud, and user, possesses several advantages. First, in terms of security, for any two data that are sparse in certain transformed domain, their corresponding ciphertexts are indistinguishable on the cloud side. Second, to avoid the communication bottleneck between the user and cloud, the sensor can encrypt data individually such that, once the cloud receives encrypted data from sensor, it can immediately carry out its task without requesting any information from the user. Third, we show that, even though the cloud knows the permuted support information of data, the security never is sacrificed. Meanwhile, the compression rate can be reduced further. Theoretical and empirical results demonstrate that our system is cost effective and privacy guaranteed and that it possesses acceptable reconstruction quality.

In this paper, we focus on the creation of general purpose 2-D image quality databases. Although there are many of them, they still lack some important characteristics, such as high-definition resolution, diversified source images, more commonly seen distortions, and a larger amount of test (distorted) images. To tackle this problem, we create a high-definition image database, which has higher resolution than most of the image quality databases. In addition, we collect 250 source images from 10 categories, which are far more diversified than other existing quality databases. Moreover, we generate 10 most commonly seen distortions to represent the real world scenario. Finally, 12000 test images are generated for the whole database, which is the largest data set so far compared with other general purpose image quality databases with human subjective ratings. The subjective test is conducted in a controlled environment to obtain the ground truth of image quality, where we collect over 360 000 opinion scores. We believe the birth of this quality database would help further development of research on image quality assessment.