Inspired by the ability of deep generative models to generate highly realistic images, much recent work has made progress in enhancing underexposed images globally. However, the local image enhancement approach has not been explored, although they are requisite in the real-world scenario, e.g., fixing local underexposure. In this work, we define a new task setting for underexposed image enhancement where users are able to control which region to be enlightened with an input mask. As indicated by the mask, an image can be divided into three areas, including Masked Area A, Transition Area B, and Unmasked Area C. As a result, Area A should be enlightened to the desired lighting, and there shall be a smooth transition (Area B) from the enlightened area (Area A) to the unchanged region (Area C). To finish this task, we propose two methods: Concatenate the mask as additional channels (MConcat), Mask-based Normlization (MNorm). While MConcat simply append the mask channels to the input images, MNorm can dynamically enhance the spatial-varying pixels, guaranteeing the enhanced images are consistent with the requirement indicated by the input mask. Moreover, MConcat serves as a play-and-plug module, and can be incorporated with existing networks, which globally enhance images, to achieve the local enhancement. And the overall network can be trained with three kinds of loss functions in Area A, Area B, and Area C, which are unified for various model structures. We perform extensive experiments on public datasets with various parametric approaches for low-light enhancement, %the Convolutional-Neutral-Network-based model and Transformer-based model, demonstrating the effectiveness of our methods.
翻译:受深层基因化模型生成高度现实图像的能力的启发,许多近期工作在提升全球未得到充分曝光的图像方面取得了进步。然而,虽然在现实世界情景中,地方图像增强方法是必需的,例如,修补本地曝光不足。在这项工作中,我们为未得到充分曝光的图像增强确定了一个新的任务设置,用户能够用输入面罩控制哪个区域开明。正如蒙面显示的那样,图像可以分为三个领域,包括蒙面区域A、过渡区域B和无印面区域C。因此,A区域图像增强方法应当开明于预期的区域照明,而从开明区域(A区域A)到无变化区域(A区域C),这些方法是必要的。为了完成这项任务,我们提出了两种方法:将遮面作为额外的渠道(MConcat)、基于Macks的Norml化(Monoral)。MConcat只是将掩码系统连接到低位图像中,Monmmt可以动态地加强空间变化图象系统;A区域的变现法和升级系统功能将加强区域图像升级为升级的模型;我们现有的模型和升级的升级模型可以显示升级的模型。