# Finding the Least Motion-Blurred Image by Reusing Early Features of Object Detection Network
## 4 Discussion
Using this model in inference mode provides a possibility to rank a group of new images according to motion blur distortions in the images and a way to find an image with the lowest motion blur. Features from the feature maps were sampled from the region that corresponds to the detected region of the head. The bounding box (BBox) of the head in the input image is scaled down the spatial position in the feature proportionally according to the size ratios of the feature map and input image. After finding a region in the feature map that corresponds to the head position, we randomly sample features from that region. Sampling randomly allows for reducing the possibility of overfitting the model to features specific to some facial regions. So it works as a regularization strategy. During inference, features are sampled uniformly in the corresponding head region. Each feature vector is passed to the LightGBM ranker model, and the outputs from the same image are averaged to get a final score that is used to select an image with the largest one. The results of the first experimental comparison (images of the mannequin head) of image quality evaluation methods show that our proposed method outperformed standard methods: sharpness-based, BRISQUE, NIQUE, and PIQUE. The best-performing variant, FM6-LightGBM of the proposed method, achieved 99.8% rank-1 accuracy, so it reliably finds a less motion-blurred (higher quality) image from the two images in the test set. This method variant was consistently the best performing in almost all pairwise image comparison tests. The variant is based on features from the 6 th convolutional layer (Conv 3-2). The only place where it was a runner-up was the first category, where the method must differentiate between I1-I2 images. These images are from a harder comparison set because their quality differs the least. The I1 image is the original image, and the I2 image is the original image affected by the smallest motion blur compared to all images in this experiment. It may be that minor motion blur distortions do not significantly affect image quality. Additionally, the original images were not reviewed to remove lower quality frames, so the minor additional distortions could be that are not imposing quality degradation of the original image. The I1-I2 comparison category was the hardest for all methods, so the lower initial quality of the frames could be the reason. Additional experiments were performed to compare the methods using images of real faces and to test the potentiality of fusing features from different feature maps. The experiment using images of real faces shows that our proposed method remained the top-performing. There were slight changes in the top positions for the variants of the proposed method--in the situation of real faces, the #8 variant (features from Conv 4-1 convolutional layer) is leading instead of the #6 (features from Conv 3-2 convolutional layer), which was in the top when images of mannequin head were used. This shift is probably due to the change in the overall quality of images but not due to the change from mannequin face to real faces. Images of the mannequin were collected by us at 2-3 times higher resolution than images of real faces from the IMDB-WIKI dataset. To perform similar experiments, images of real faces were upsampled to be of similar resolution to the images of the mannequin head. Consequently, images of real faces contain less higher-frequency components, which are detected in earlier feature maps. The case of lower levels of higher-frequency components additionally explains why the performance of the sharpness-based method dropped in this case. Experiments on feature pooling from different feature maps show that feature combination is beneficial and increases the performance of our proposed method.