Underwater images present blur and color cast, caused by light absorption and scattering in water medium. To restore underwater images through image formation model (IFM), the scene depth map is very important for the estimation of the transmission map and background light intensity. In this paper, we propose a rapid and effective scene depth estimation model based on underwater light attenuation prior (ULAP) for underwater images and train the model coefficients with learning-based supervised linear regression. With the correct depth map, the background light (BL) and transmission maps (TMs) for R-G-B light are easily estimated to recover the true scene radiance under the water. In order to evaluate the superiority of underwater image restoration using our estimated depth map, three assessment metrics demonstrate that our proposed method can enhance perceptual effect with less running time, compared to four state-of-theart image restoration methods.

Light absorption and scattering lead to underwater image showing low contrast, fuzzy, and color cast. To solve these problems presented in various shallow-water images, we propose a simple but effective shallow-water image enhancement method-relative global histogram stretching (RGHS) based on adaptive parameter acquisition. The proposed method consists of two parts: contrast correction and color correction. The contrast correction in RGB color space firstly equalizes G and B channels and then redistributes each R-G-B channel histogram with dynamic parameters that relate to the intensity distribution of original image and wavelength attenuation of different colors under the water. The bilateral filtering is used to eliminate the effect of noise while still preserving valuable details of the shallow-water image and even enhancing local information of the image. The color correction is performed by stretching the 'L' component and modifying 'a' and 'b' components in CIE-Lab color space. Experimental results demonstrate that the proposed method can achieve better perceptual quality, higher image information entropy, and less noise, compared to the state-of-the-art underwater image enhancement methods.

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With the growing awareness of data privacy, more and more cloud users choose to encrypt their sensitive data before outsourcing them to the cloud. Search over encrypted data is therefore a critical function facilitating efficient cloud data access given the high data volume that each user has to handle nowadays. Inverted index is one of the most efficient searchable index structures and has been widely adopted in plaintext search. However, securing an inverted index and its associated search schemes is not a trivial task. A major challenge exposed from the existing efforts is the difficulty to protect user's query privacy. The challenge roots on two facts: 1) the existing solutions use a deterministic trapdoor generation function for queries; and 2) once a keyword is searched, the encrypted inverted list for this keyword is revealed to the cloud server. We denote this second property in the existing solutions as one-timeonly search limitation. Additionally, conjunctive multi-keyword search, which is the most common form of query nowadays, is not supported in those works. In this paper, we propose a public-key searchable encryption scheme based on the inverted index. Our scheme preserves the high search efficiency inherited from the inverted index while lifting the one-time-only search limitation of the previous solutions. Our scheme features a probabilistic trapdoor generation algorithm and protects the search pattern. In addition, our scheme supports conjunctive multi-keyword search. Compared with the existing public key based schemes that heavily rely on expensive pairing operations, our scheme is more efficient by using only multiplications and exponentiations. To meet stronger security requirements, we strengthen our scheme with an efficient oblivious transfer protocol that hides the access pattern from the cloud. The simulation results demonstrate that our scheme is suitable for practical usage with moderate overhead.

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