Relative colour constancy is an essential requirement for many scientific imaging applications. However, most digital cameras differ in their image formations and native sensor output is usually inaccessible, e.g., in smartphone camera applications. This makes it hard to achieve consistent colour assessment across a range of devices, and that undermines the performance of computer vision algorithms. To resolve this issue, we propose a colour alignment model that considers the camera image formation as a black-box and formulates colour alignment as a three-step process: camera response calibration, response linearisation, and colour matching. The proposed model works with non-standard colour references, i.e., colour patches without knowing the true colour values, by utilising a novel balance-of-linear-distances feature. It is equivalent to determining the camera parameters through an unsupervised process. It also works with a minimum number of corresponding colour patches across the images to be colour aligned to deliver the applicable processing. Two challenging image datasets collected by multiple cameras under various illumination and exposure conditions were used to evaluate the model. Performance benchmarks demonstrated that our model achieved superior performance compared to other popular and state-of-the-art methods.
翻译:然而,大多数数字相机在图像形成方面各不相同,本地感应器输出通常无法进入,例如智能手机应用。这使得很难对各种设备进行一致的颜色评估,从而损害计算机视觉算法的性能。为了解决这个问题,我们提议了一个颜色校正模型,将相机图像形成视为黑盒,并将颜色校正作为一个三步进程:相机反应校准、反应线性化和颜色匹配。拟议的模型使用非标准颜色参考,即没有了解真正颜色值的颜色补丁,使用一种新的线性平衡距离特征。这相当于通过非超强程序确定相机参数。我们还提议了一个颜色校正模型,将相机图像的相配配合点作为黑盒,并将颜色配对成一个三步进程:摄影机反应校准、反应线性直线化和颜色匹配。使用了两个挑战性的图像数据集来评估模型。业绩基准表明,我们的模型与其他流行和状态方法相比,其性能优于其他流行和状态方法。