Deep No-Reference Quality Assessment for Underwater Enhanced Images

The goal of underwater image enhancement (UIE) is to boost the acquired underwater image quality, which increases the value of the underwater image significantly. However, without effective underwater enhanced image quality assessment (UEIQA) measures that benchmark the UIE, the process of UIE becomes driftless and the enhanced results of different UIE algorithms cannot be fairly compared. Toward this end, we in this work construct a dedicated UEIQA scheme on the basis of deep investigation of the underwater enhanced image characteristics. Specifically, in our proposed method, we respectively design deep neural networks to represent the unique attributes of the underwater enhanced image, such as color cast, local distortions, naturalness degree, sharpness, contrast, fog density, etc., that are highly correlated with the image quality. Then we introduce the Vision Transformer (ViT) to capture the dependencies among different image attributes and infer the image quality level. Extensive experiments conducted on three typical UEIQA databases, i.e., SOTA, UID2021 and SAUD, show that the proposed UEIQA model yields noteworthy higher prediction accuracy than the representative IQA and UEIQA metrics, e.g., achieving SRCC values of 0.891 ( vs. 0.749 in SAUD) and 0.933 ( vs. 0.798 in UID2021). The proposed UEIQA model will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/YT2015?tab=repositories</uri>.