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Owing to the rapid development of deep networks, single image deraining tasks have achieved significant progress. Various architectures have been designed to recursively or directly remove rain, and most rain streaks can be removed by existing deraining methods. However, many of them cause a loss of details during deraining, resulting in visual artifacts. To resolve the detail-losing issue, we propose a novel unrolling rain-guided detail recovery network (URDRN) for single image deraining based on the observation that the most degraded areas of the background image tend to be the most rain-corrupted regions. Furthermore, to address the problem that most existing deep-learning-based methods trivialize the observation model and simply learn an end-to-end mapping, the proposed URDRN unrolls the single image deraining task into two subproblems: rain extraction and detail recovery. Specifically, first, a context aggregation attention network is introduced to effectively extract rain streaks, and then, a rain attention map is generated as an indicator to guide the detail-recovery process. For a detail-recovery sub-network, with the guidance of the rain attention map, a simple encoder–decoder model is sufficient to recover the lost details. Experiments on several well-known benchmark datasets show that the proposed approach can achieve a competitive performance in comparison with other state-of-the-art methods.

Images captured from underwater environment always suffer from color distortion, detail loss, and contrast reduction due to the medium scattering and absorption. This paper introduces an enhancement approach to improve the visual quality of underwater images, which does not require any dedicated devices or additional information more than the native single image. The proposed strategy consists of two steps: an improved white-balancing approach and an artificial multiple underexposure image fusion strategy for underwater imaging. In our white-balancing approach, the optimal color-compensated approach is determined by the sum of the Underwater Color Image Quality Evaluation (UCIQE) and the Underwater Image Quality Measure (UIQM). We get an optimal white-balanced version of the input by combining the well-known Gray World assumption and the optimal channel-compensated approach. In our artificial multiple underexposure image fusion strategy, first the gamma-correction operation is adopted to generate multiple underexposure versions. Then we propose to use `contrast', `saturation', and `well-exposedness' as three weights, to be blended into the well-known multi-scale fusing scheme. Images enhanced by our strategy have a better visual quality than some state-of-the-art underwater dehazing techniques, through our validation with a wide range of qualitative and quantitative evaluations.

We present a novel solution to enable portrait relief modeling from a single image. The main challenges are geometry reconstruction, facial details recovery and depth structure preservation. Previous image-based methods are developed for portrait bas-relief modeling in 2.5D form, but not adequate for 3D-like high relief modeling with undercut features. In this paper, we propose a template-based framework to generate portrait reliefs of various forms. Our method benefits from Shape-from-Shading (SFS). Specifically, we use bi-Laplacian mesh deformation to guide the relief modeling. Given a portrait image, we first use a template face to fit the portrait. We then apply bi-Laplacian mesh deformation to align the facial features. Afterwards, SFS-based reconstruction with a few user interactions is used to optimize the face depth, and create a relief with similar appearance to the input. Both depth structures and geometric details can be well constructed in the final relief. Experiments and comparisons to other methods demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposed an image enhancement method based on multi-layer fusion and detail recovery, to solve the image deterioration such as low contrast and blurred details in undesirable illumination environments. Firstly, this paper copied the V channel equivalently into three layers in HSV color space: Retinex enhancement layer, brightness enhancement layer, detail enhancement layer. In Retinex enhancement layer, this paper combined with weighted guided image filtering and morphology to eliminate halo phenomenon. It improved Retinex model to enhance brightness and details of images. In detail enhancement layer, this paper used artificial bee colony algorithm to optimize improved model of local linear to obtain more details. Finally, this paper performed Gamma correction and pixel arrangement to avoid partial fuzzy details caused fusion. The experimental results show that the proposed method can more effectively highlight image details and improve the contrast. The comprehensive performance is superior while comparing with the related methods in terms of objective quantification, especially in Tenengrad index. [ABSTRACT FROM AUTHOR]