# Dealing with Low Quality Images in Railway Obstacle Detection System
## 3 State-of-the-Art Techniques for Blur Detection
### Frequency-Based Methods  
The most common frequency-based method examines the distribution of low and high frequencies using some variant of the fast Fourier transform of the image. The image can be regarded as being blurry if the number of high frequencies is low. High frequencies are related to the dynamic image with a lot of edges and great color differences of neighboring pixels. However, distinguishing what constitutes a low number of high frequencies and what constitutes a high number of high frequencies can be difficult, leading to subpar results when judging whether or not a picture is blurry. When a low value is utilized, images may be categorized as blurry when, in fact, they are not. On the other hand, when the threshold is set too high, images may be mistakenly labeled as clear when they are not. The authors of [20] described a method for assessing no-reference blur in natural images. The presented method uses information derived from the power spectrum of the Fourier transform to estimate the distribution of low and high frequencies. To categorize photos as blurred, an image blur quality evaluator is created by applying a support vector machine (SVM) classifier. Ref. [21] presented yet another way of employing SVM for BD. In that paper, a blur identification problem is described as a multiclass classification problem that can be handled using SVM for both horizontal motion blur and atmospheric turbulence blur. In [22], a novel method for detecting motion-blurred regions is described. The approach for estimating motion direction on blurred images provided here is based on measuring the lowest directional high-frequency energy. The collected findings revealed that the proposed strategy improved accuracy while requiring less computational time. The authors in [23] proposed a new blur metric based on multiscale singular value decomposition for the detection of defocus regions in an image, inspired by the fact that the degree of defocus blur depth might be discriminated by distinct frequencies. This method was found to considerably reduce the likelihood of false positives in BD and to overcome the problem of the sharp region being misinterpreted as a blur zone due to its smooth texture. Algorithm S\({}_{3}\) (spectral and spatial sharpness) that uses spectral and spatial properties to measure local perceived sharpness in images is presented in [24]. It was demonstrated that the suggested technique can assess local perceived sharpness inside and across images without requiring the presence of edges. Furthermore, the authors proved that the resulting sharpness map can be condensed into a scalar index that quantifies the total perceived sharpness of a picture. The probability model for non-blurred natural images based on local Fourier transform is presented in [25]. It was demonstrated that, despite its simplicity, this model is capable of accurately determining if a small image window is blurred. The authors presented an approach for detecting blurriness using the log averaged spectrum residual in [26]. The proposed approach was proved to work effectively for both defocus and motion blur. In addition, to distinguish between the in-focus smooth area and blurred smooth region, an iterative update mechanism was devised. One more approach that was proven to be effective for both types of blur is presented in [27]. The proposed method is based on a revolutionary high-frequency multiscale fusion and sort transform of gradient magnitudes. The high-frequency DCT coefficients for each resolution are extracted and then merged in a vector to calculate the level of blur at each image spot.