恶劣复杂及对抗环境下产生的图像数据通常受限，常具有非完整、不确定、小样本、小目标的全部或部分特性，与通常的图像大数据相比，对受限图像数据处理和分析的方法有明显区别：大数据的统计特性显著依赖于中心极限定理下的3σ原则，而受限下的图像数据统计一致性弱，难以体现可信且鲁棒的集中优势特点；遮挡、伪装等情况导致样本信息乃至维度都具有不完整性或非确定性，以模糊数学为基础的系列处理方法导致计算量剧增；以深度学习为代表的系列大数据处理方法得到巨大发展，但由于受限图像数据的处理和分析基本属于不可逆的逆问题，其解空间一般为高维子空间，如何可信鲁棒地确定空间中的解，目前尚无有效可行的通用理论和方法；采用隶属度为测度的系列方法需紧密依赖融入先验知识构造的代价函数。为促进受限图像数据的研究，梳理了对其处理和分析的机理、方法、手段以及遇到的困难，提出了可能的突破方法，以及催生的研究范式改变，以求抛砖引玉，吸引更多学者从事该研究。

Image big data and easy-use models such as deep networks(DNs) have expedited artificial intelligence (AI) currently. But, the issue for limited images which are often captured from complex, hostile and other scenarios has been challenging still yet:objects are too small to be recognized; the boundaries are fuzzy-overlapped; or even all the object information indicated in the images is uncertain due to camouflages and occlusions. The limited image data are featured with small-sample, small-object, incompleteness, uncertainty. Obviously, the tackle of limited image data is different from image big data:1) image big data fits Gaussian distributions (mean u and σ) in terms of statistical central limit theorem, especially when data scales are much larger than data dimensions. This feature is beneficial to make statistical inference by the 3σ rule, which indicates that 99.73% of samples are within the range of[u-3σ, u+3σ]. Possibly due to the concentration saliency fundamental, the statistical AI models such as DNs become very popular and seemly successful. However, for small dataset, the statistical consistency is usually poor, and the robust features cannot be identified based on concentration saliency. Thus, the statistical inference AI models are not feasible for small training data. 2) Image objects are often mutually occluded in costly and rare scenarios, and the delicate camouflages and masks, poor and complex luminous environment, as well as hostile disturbances, often make the image information itself or the information dimensions incomplete and unconfident. These issues make the computation load very heavy because the missed information or dimension has a large number of possibilities. 3) A large number of big data techniques have been proposed and seem very competitive in image processing field. For example, the DNs have achieved the best rank in many image big datasets publicly available. These features have extremely highlighted the contribution of DNs. However, many face recognition systems do not meet the requirement of accuracy and precision even when the face data is big enough. The statistical inference models such DNs cannot do precise inference, and some errors do exist due to the statistical inference itself. For limited image data, the consequence has been declined further especially when the number of samples is less than the dimensions. The partition boundaries in the sample space cannot be uniquely fixed by training samples. That is to say, to determine the inference model needs to solve irreversible inverse problems, meaning that the definite model cannot be uniquely fixed, and only a reduced model can be fixed. For any query sample, the simplified model cannot give an explicit solution, and only a subspace can just be given which is constituted of possible solutions. How to choose an appropriate solution from the subspace is still challenging, and seemingly there are no effective and general ways. Many techniques seem a little effective on studying limited image dataset, such as level-set methods, fussy logic methods, all these methods are based on the probability metrics measuring the divergence between the existent limited image data and the apriori knowledge or the specific background. That is to say, the cost functions indicating membership degrees, levels etc. are very critical for these methods.