We introduce a camera pipeline for rendering visually pleasing photographs in low light conditions, as part of the NTIRE2022 Night Photography Rendering challenge. Given the nature of the task, where the objective is verbally defined by an expert photographer instead of relying on explicit ground truth images, we design an handcrafted solution, characterized by a shallow structure and by a low parameter count. Our pipeline exploits a local light enhancer as a form of high dynamic range correction, followed by a global adjustment of the image histogram to prevent washed-out results. We proportionally apply image denoising to darker regions, where it is more easily perceived, without losing details on brighter regions. The solution reached the fifth place in the competition, with a preference vote count comparable to those of other entries, based on deep convolutional neural networks. Code is available at www.github.com/AvailableAfterAcceptance.
翻译:我们引入了在低光条件下拍摄令人愉快的相片的摄像管,作为NTIRE2022夜间摄影展示挑战的一部分。鉴于任务的性质,即目标由专家摄影师口头界定,而不是依赖明确的地面真相图像,我们设计了一个手工制作的解决方案,其特点是浅色结构和低参数计数。我们的管道利用当地光亮增强器作为高动态范围校正的一种形式,随后对图像直方图进行全球调整,以防止被冲出的结果。我们按比例将图像去除在较黑暗的地区,在这些地区更容易看到,同时不丢失更亮区域的细节。解决方案在竞争中达到了第五位,优先票数与其他条目的票数相比,以深革命性神经网络为基础。代码可在www.github.com/AvailableAfonAcceptance查阅。