Nonlinear spike-and-slab sparse coding for interpretable image encoding. PLOS ONE

2) Realistic occlusion dataset This dataset contains realistic occlusions and controlled forms of sparse structure -- it is a realistic artificial dataset consisting of true occlusions (data is created by actual occlusions and not following any computational model for its generation). The data was generated using the Python Image Library (PIL) to draw hundreds of overlapping edges/strokes in a 256 × 256 pixel image: each stroke had an integer intensity between (1, 255), a width between (2, 4) pixels, and a length, starting, and ending position drawn independently from a uniform distribution. The image was then cut into overlapping D = 9 × 9 patches, each of which contained k ∈ (0, 5) overlapping strokes, for N = 61009. Gaussian noise of σ = 25 and μ = 0 was then independently added to each patch. Additionally, the dataset also contains the corresponding (automatically obtained) labels for each image, indicating the ground-truth number of occluding strokes k ∈ (0, 5) per image. Such a dataset represents and isolates challenging aspects of low-level image statistics that are present in all natural images. Particularly, it contains edges of varying intensities and their occlusions.<br>In the corresponding publication, data examples are shown in Figure 5.

2) 真实遮挡数据集。本数据集包含真实遮挡效果与可控形式的稀疏结构——其为采用真实遮挡构建的人工真实数据集，生成过程未遵循任何预设计算模型。数据生成过程借助Python图像库（Python Image Library，PIL）在256×256像素的画布上绘制数百条重叠的边缘/笔触：每条笔触的强度为1至255之间的整数，线宽为2至4像素，其长度、起始位置与终止位置均从均匀分布中独立随机采样得到。随后将该图像切割为D=9×9的重叠图像块，共计N=61009个，每个块中包含k∈(0,5)条重叠笔触。随后为每个图像块独立添加均值为μ=0、标准差为σ=25的高斯噪声。此外，本数据集还为每张图像提供了（自动获取的）对应标签，用于标注每张图像中遮挡笔触的真实数量k∈(0,5)。该数据集能够表征并隔离出所有自然图像中普遍存在的低层次图像统计特性中的挑战性场景，具体而言，其包含不同强度的边缘及其遮挡效果。
在相关发表文献中，数据集示例展示于图5。