It is "prior information" on your set of images, that you can use in your image processing problems to enhance results, ease the choice of processing parameters, resolve indeterminacies, publish fancy papers no one ever use, etc.
For instance, you may know that the image, albeit noisy, should contain only 4 colors. Or that pixels follow a specific distributions. These priors, or their approximations, can be put into math form and can be merged into the processing (filtering, deconvolution, segmentation), and reduce the set of feasible solutions, generally through optimization algorithms.
它是图像集上的“先验信息”，您可以在图像处理问题中使用它来增强结果，简化处理参数的选择，解决不确定性，发表从未有人使用过的华丽论文等。
例如，您可能知道图像，尽管有噪声，但应该只包含4种颜色。或者像素遵循特定的分布。这些先验或它们的近似值可以用数学形式表示，并可以合并到处理(滤波、反卷积、分割)中，通常通过优化算法减少可行解集。