# Storytelling with Image Data: A Systematic Review and Comparative Analysis of Methods and Tools
## 3 Task 1. Building the Foundation for Organizing and Curating the Raw Image Data
### Curation: Image Preparation Using Cleaning and Preprocessing
#### 3.1.1 Image Correction  
Prior to feature generation and analysis, potential noises in the images may need to be addressed during image preprocessing. Various candidates for rectification are listed below.  
* **Image Denoising**[13; 14]. Noise might be created randomly or through the operation of a device. The presence of noise distorts the photos and may confuse the identification process. Thus, image denoising is a critical preprocessing approach for the subsequent phases of image processing. Traditionally used single-image denoising methods frequently represent the image's attributes and the noise they are intended to remove analytically. In contrast, contemporary denoising techniques frequently use neural networks to develop a mapping between noisy and noise-free pictures. While DL models are able to reflect complex features of pictures and noise, they need considerable paired samples to train such systems. Therefore, the majority of denoising approaches that are based on learning make use of synthetic training datasets.
* **Color Corrections**[15]. Given that digital photographs are inherently color images, such examination should make proper use of the color or spectral data contained in the acquired data. However, prior to performing color picture analysis, considerable preprocessing is often required to both enhance and facilitate effective analysis. Color calibration is used to guarantee that correct color information is collected. In contrast, color enhancing ensures the efficient results of image analysis techniques that are otherwise highly susceptible to imaging circumstances and scanner fluctuations. The approaches used vary from calibrating cameras and scanners to adjusting the presented colors for converting the picture to another color representation, which can help with following activities such as segmentation. The color technique would be utilized to effectively distinguish the achievements of stains that are all in the same region. This allows the assessment of strain-specific pictures, whereas color normalization methods can be used to decrease different shades in histopathology images caused by things such as scanner features, chemical color levels, or different procedures.
* **Lighting Corrections**[16; 17]. Deep shadows cast by lighting can hide particular textural characteristics; inconsistent lighting across the picture might distort findings. Rank filtering, histogram equalization, and remapping are all possible corrective approaches. Background can be caused by uneven illumination, which occurs when the light source is on one side of the picture, and the overall brightness decreases from one side to another. However, there are other different methods to establish a background, and frequently, the background is so faint or normal that we have difficulty noticing it at all. To address background issues, it is good to obtain a separate image of the background. This method compensates for lighting issues and additional background abnormalities.