11/8/2022 0 Comments Deep photoenhancer cv![]() To enable an in-depth understanding of deep SOD, in this paper, we provide a comprehensive survey covering various aspects, ranging from algorithm taxonomy to unsolved issues. Recent advances in SOD are predominantly led by deep learning-based solutions (named deep SOD). Īs an essential problem in computer vision, salient object detection (SOD) has attracted an increasing amount of research attention over the years. Furthermore, the potential benefits of our Zero-DCE to face detection in the dark are discussed. Extensive experiments on various benchmarks demonstrate the advantages of our method over state-of-the-art methods qualitatively and quantitatively. Despite its simplicity, we show that it generalizes well to diverse lighting conditions. Our method is efficient as image enhancement can be achieved by an intuitive and simple nonlinear curve mapping. This is achieved through a set of carefully formulated non-reference loss functions, which implicitly measure the enhancement quality and drive the learning of the network. ![]() Zero-DCE is appealing in its relaxed assumption on reference images, i.e., it does not require any paired or unpaired data during training. ![]() The curve estimation is specially designed, considering pixel value range, monotonicity, and differentiability. Our method trains a lightweight deep network, DCE-Net, to estimate pixel-wise and high-order curves for dynamic range adjustment of a given image. The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network. ![]()
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