## 1 Introduction
Differing from satellite images, another group of optical data in need of enhancement is historical aerial photographs obtained with photogrammetric techniques. In general, a historical aerial photograph for which the visual interpretation capacity can be increased with SR techniques can be considered as LR. The quality of a historical aerial photograph is conditioned by the scenario as well as by environmental, lighting, and atmospheric conditions [41]. It is possible to lose information when converting these photographs from hard copy versions to digital form through scanning procedures. Thus, in addition to the SR problems that satellite images bear, historical aerial photographs have additional restrictions. One of the factors that further limits the use of historical aerial photographs compared to other optical images and their improvement using SR techniques is the number of spectral bands. In the literature, the images used in SR studies consist of multispectral data. SR models simultaneously improve the available RGB spectral bands in multispectral images; moreover, as of today, attention structures are able to combine the prominent features of each band, increasing the quality of the resulting images. Furthermore, the use of infrared bands in enhancing LR images is possible. However, this is not likely in most historical aerial photographs, as they have only a single band. Another limiting factor is the lack of color information in historical aerial photo-graphs, which is one of the distinguishing features used for differentiating earth objects. When two objects that are identical or similar in shape and size are both grayscale, their difference diminishes. In addition to the similarities in the same LULC details, the resemblance of these details is a situation that negatively affects the SR model's ability to train different features. This makes it harder to recover information possibly lost in the LR image. Theoretically, a single band image is the same as being grayscale. The reason why it is considered separately as a limiting factor in this study is that the number of bands is directly related to the running of the algorithm; however, the color information creates distinguishability of objects and differences among them. To avoid repetition, only the term grayscale is used for the data used in the rest of the study. Other factors that make the improvement of historical aerial photographs more challenging are the current image quality and the limitation of the original image in terms of LULC diversity due to the acquired time. Because old dates are involved, certain LULC classes in current datasets may be limited or absent in historical photographs. In addition, HR and LR image pairs for historical aerial photographs are not exactly similar to the datasets present in the literature. A historical aerial photograph which is considered to be theoretically HR may be objectively LR compared to current optical images. Therefore, it is theoretically more difficult for a model to learn the difference between two attributes and recover the lost information. The literature is lacking a detailed study on improving grayscale historical aerial photographs within the scope of this research, which is important for solving real-world problems related to the past. Historical aerial photographs can be traced back 100 years; as it spans such a large time range, this data group is extremely useful for long-term monitoring of the environment. When this monitoring includes change detection applications where historical data should be analyzed together with current and high spatial resolution datasets, they need to be improved spatially in order to achieve more reliability. In essence, improved historical data should enable efficient visual interpretation and, if necessary, detailed object extraction. However, restrictive factors arising from the nature of the data make it difficult for the SR model to learn the relationships between HR-LR pairs. As a result, the smoothing effect becomes evident in the improved image.