Motion deblurring based on deep feature fusion attention and double-scale
When taking an image, the image tends to come out blurred due to interferences from shaky or out of focus lenses, dust, or atmospheric light. Image blurs can be categorized into atmospheric, defocus, and motion blurs. The most common type of blur is motion blur, which is caused by jittering objects during shooting and has a great impact on computer vision tasks, such as image classification, object detection, text recognition. On the one hand, non-blind deblurring has a known fuzzy kernel and can output a sharp image due to effect of deconvolution with fuzzy image directly after removing noise. This method is simple and effective, but its premise is that the fuzzy kernel is known. On the other hand, blind deblurring faces the serious problem where the fuzzy kernel is unknown. method. The method is complicated, computationally expensive, and time consuming. However, the fuzzy kernel is unknown in real scenarios, and most cases of deblurring are actually blind deblurring. Therefore, this paper focuses on blind deblurring. To address motion blur, a two-scale network based on deep feature fusion attention is proposed in this paper. First, a two-scale network is designed to extract different scales of spatial information. During the transformation from high to low scale, the state of blurred features in the motion blurred image changes from smooth to sharp. Therefore, the network pays further attention to those fuzzy areas in a low-scale image, hence allowing the network to obtain fuzzy features. This network not only improves the capability of recovering frequency details from the original scale image but also effectively uses the spatial information of the blurred image to enhance the deblurring effect of the model. Second, the deep feature fusion attention module is constructed. The main structure of the network is very similar to that of U-Net. After the encoding and decoding structure, the deep feature fusion attention module is constructed to obtain the best fusion feature information. fusion attention module to produce full-scale features, which in turn are spliced and fused with the decoded features of the same level to further enhance the recovery performance of the network. Third, in order to make the network recover the high-frequency details effectively, the function of perception loss is replaced by the function of frequency loss. The loss function in this paper is composed of two parts, namely, content loss and frequency loss. content loss. This procedure also marks the first step in calculating the 2D Fourier transform of the restored and sharp images and in measuring the frequency loss. After calculating the Fourier transform, the average absolute error between the restored and sharp images is determined. The multi-scale frequency loss is obtained by multiplying the multi-scale weight by the average absolute error. We compare the performance of our model with that of 12 other methods on 3 different datasets. We evaluate the performance of our model on the GoPro dataset by utilizing the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) between the restored and sharp images. Compared with the scale-recurrent network, the proposed method obtains 2.29 dB higher PSNR, thus allowing this method to recover detailed information. We also compare the optimal results of each method. We test the generalization performance of the proposed method on the Kohler dataset, where this method achieves the highest PSNR of 29.91 dB without retraining. Meanwhile, we compare the deblurring performance of these methods for real blurred images on the Lai dataset and determine the best results via subjective comparison. To improve the quality of motion deblurring, a two-scale network based on deep feature fusion attention is proposed in this paper. This work offers three contributions, namely, a novel two-scale network, a deep feature fusion attention module, and multi-scale loss. Objective and subjective experimental results show that the proposed deblurring model can efficiently integrate image spatial information and feature semantic information, thus improving its deblurring performance, PSNR, and SSIM.
