Due to the attenuation and scattering of light by water, there are many quality defects in raw underwater images such as color casts, decreased visibility, reduced contrast, et al. . Many different underwater image enhancement (UIE) algorithms have been proposed to enhance underwater image quality. However, how to fairly compare the performance among UIE algorithms remains a challenging problem. So far, the lack of comprehensive human subjective user study with large-scale benchmark dataset and reliable objective image quality assessment (IQA) metric makes it difficult to fully understand the true performance of UIE algorithms. We in this paper make efforts in both subjective and objective aspects to fill these gaps. Firstly, we construct a new Subjectively-Annotated UIE benchmark Dataset (SAUD) which simultaneously provides real-world raw underwater images, readily available enhanced results by representative UIE algorithms, and subjective ranking scores of each enhanced result. Secondly, we propose an effective No-reference (NR) Underwater Image Quality metric (NUIQ) to automatically evaluate the visual quality of enhanced underwater images. Experiments on the constructed SAUD dataset demonstrate the superiority of our proposed NUIQ metric, achieving higher consistency with subjective rankings than 22 mainstream NR-IQA metrics. The dataset and source code will be made available at https://github.com/yia-yuese/SAUD-Dataset .

According to the types of the used features, state-ofthe-art methods for blind image quality assessment (BIQA) typically have two categories. The first category utilizes handcrafted natural scene statistics (NSS) features which are derived based on the statistical regularity of natural images. The second category utilizes codebook-based features which are obtained by feature encoding over a pre-constructed codebook. However, several problems need to be addressed in the existing codebook-based BIQA methods. First, dimension of the involved codebook-based features is too high. Second, codebooks are usually constructed by unsupervised learning algorithms. To address these problems, we propose a novel codebook-based BIQA method by optimizing multi-stage discriminative dictionaries (MSDDs). To be more specific, MSDDs are learned by performing the label consistent K-SVD (LC-KSVD) algorithm in a stage-by-stage manner. In each stage, a new quality consistency constraint called “qualitydiscriminative regularization” term is introduced and incorporated into the traditional reconstruction error term to form a unified objective function which can be effectively solved by LC-KSVD. Then, the latter stage takes the reconstruction residual data in the former stage as input based on which LC-KSVD is repeatedly performed until the final stage is reached. Once the MSDDs are learned, multi-stage feature encoding (MSFE) is performed to extract feature codes. Finally, the feature codes are concatenated across all stages and spatially aggregated over the entire image for quality prediction via a typical regression module. The proposed method has been extensively evaluated on five IQA databases and the experimental results has well confirmed its superiority over the existing relevant BIQA methods.

Perceptual quality assessment is a challenging issue in 3D signal processing research. It is important to study 3D signal directly instead of studying simple extension of the 2D metrics directly to the 3D case as in some previous studies. In this paper, we propose a new perceptual full-reference quality assessment metric of stereoscopic images by considering the binocular visual characteristics. The major technical contribution of this paper is that the binocular perception and combination properties are considered in quality assessment. To be more specific, we first perform left-right consistency checks and compare matching error between the corresponding pixels in binocular disparity calculation, and classify the stereoscopic images into non-corresponding, binocular fusion, and binocular suppression regions. Also, local phase and local amplitude maps are extracted from the original and distorted stereoscopic images as features in quality assessment. Then, each region is evaluated independently by considering its binocular perception property, and all evaluation results are integrated into an overall score. Besides, a binocular just noticeable difference model is used to reflect the visual sensitivity for the binocular fusion and suppression regions. Experimental results show that compared with the relevant existing metrics, the proposed metric can achieve higher consistency with subjective assessment of stereoscopic images.

Vision-based intelligent driving assistance systems and transportation systems can be improved by enhancing the visibility of the scenes captured in extremely challenging conditions. In particular, many low-image image enhancement (LIE) algorithms have been proposed to facilitate such applications in low-light conditions. While deep learning-based methods have achieved substantial success in this field, most of them require paired training data, which is difficult to be collected. This paper advocates a novel Unsupervised Decomposition and Correction Network (UDCN) for LIE without depending on paired data for training. Inspired by the Retinex model, our method first decomposes images into illumination and reflectance components with an image decomposition network (IDN). Then, the decomposed illumination is processed by an illumination correction network (ICN) and fused with the reflectance to generate a primary enhanced result. In contrast with fully supervised learning approaches, UDCN is an unsupervised one which is trained only with low-light images and corresponding histogram equalized (HE) counterparts (can be derived from the low-light image itself) as input. Both the decomposition and correction networks are optimized under the guidance of hybrid no-reference quality-aware losses and inter-consistency constraints between the low-light image and its HE counterpart. In addition, we also utilize an unsupervised noise removal network (NRN) to remove the noise previously hidden in the darkness for further improving the primary result. Qualitative and quantitative comparison results are reported to demonstrate the efficacy of UDCN and its superiority over several representative alternatives in the literature. The results and code will be made public available at https://github.com/myd945/UDCN .

A challenging problem in the no-reference quality assessment of multiply distorted stereoscopic images (MDSIs) is to simulate the monocular and binocular visual properties under a mixed type of distortions. Due to the joint effects of multiple distortions in MDSIs, the underlying monocular and binocular visual mechanisms have different manifestations with those of singly distorted stereoscopic images (SDSIs). This paper presents a unified no-reference quality evaluator for SDSIs and MDSIs by learning monocular and binocular local visual primitives (MB-LVPs). The main idea is to learn MB-LVPs to characterize the local receptive field properties of the visual cortex in response to SDSIs and MDSIs. Furthermore, we also consider that the learning of primitives should be performed in a task-driven manner. For this, two penalty terms including reconstruction error and quality inconsistency are jointly minimized within a supervised dictionary learning framework, generating a set of quality-oriented MB-LVPs for each single and multiple distortion modality. Given an input stereoscopic image, feature encoding is performed using the learned MB-LVPs as codebooks, resulting in the corresponding monocular and binocular responses. Finally, responses across all the modalities are fused with probabilistic weights which are determined by the modality-specific sparse reconstruction errors, yielding the final monocular and binocular features for quality regression. The superiority of our method has been verified on several SDSI and MDSI databases.