Due to problems such as edge blurriness and noise interference in liver medical image segmentation, there are still some challenges for automated liver and liver tumor segmentation. To address these problems, we propose a hybrid network called U-Trans Net, which combines Convolutional Neural Networks (CNNs) and Transformer models. In the encoding stage, it utilizes parallelly CNN and Transformer branches for multi-scale feature extraction, allowing the fusion of global local and local information in a hierarchical manner. The resulting enhanced feature representation is then used for prediction through decoding. Experimental results on the LiTS-ISBI2017 dataset demonstrated that our method could improve segmentation accuracy.
This paper focuses on traditional deep learning-based no-reference (or reference-based) image quality assessment (IQA) methods, enhancing them from the perspective of image feature extraction. It replaces the VGG16 network with the ResNet50 network for feature extraction and uses the Global Average Pooling (GAP) layer instead of FC512. Subsequently, it computes the weighted average of quality scores for different parts of the image to obtain the overall image quality. Specifically, the paper first preprocesses images by cropping, flipping, mirroring, tilting, and other methods to expand the image dataset and make it more reflective of real-world scenarios. Then, it utilizes the ResNet50 network for feature extraction, showing superior performance compared to the VGG network. Finally, a weighted pooling method is employed to derive the ultimate image score. On the TID2013 and CLIVE datasets, the Pearson Linear Correlation Coefficient (PLCC) values are 0.877 and 0.7095, respectively, while the Spearman Rank Order Correlation Coefficient (SROCC) values are 0.8510 and 0.6956. These values surpass those obtained using traditional algorithms like SSIM and GSMD, indicating the superior predictive performance of the new algorithm. Moreover, the proposed algorithm demonstrates advantages in speed and accuracy, meeting real-time application requirements more effectively.
Aiming at the problems of image distortion and detail information loss occurring in recent dehazing algorithms, this paper proposes a dehazing algorithm based on dark channel prior with multi-scale weighted transmission fusion and self-adaptive gamma correction. Firstly, in order to refine the transmission map, a multi-scale weighted transmission fusion strategy with three scales is applied in the transmission estimation step. Then, a self-adaptive gamma correction method is proposed to enhance the contrast performance after applying the multi-scale weighted transmission fusion to image dehazing, and finally get the desired dehazed image. Experimental results demonstrated that the proposed algorithm can not only overcome the problems of image distortion and detail information loss well, but also yields a satisfied performance in comparison with tested similar methods.
Tablet images are significance vehicles for ancient culture heritage. However, due to natural or artificial destruction, there usually exists a large amounts of noises or scratches in the ancient tablet images, and this makes the recognition of interesting objects carved in the ancient very difficult. To deal with this problem, a method based on transfer learning of DnCNN De-noiser Prior was proposed in this paper. Firstly all parameters of all layers of a DnCNN pre-trained in natural images are transferred to our target networks. The initial trained CNN filter weights were then fine tuned with noised Chinese tablet calligraphy images by back-propagation so that they better reflected the noise modalities of tablet image, where Chinese tablet calligraphy structures are concerned to remove isolated small scratches by combing the connected region technique with DnCNN transfer denoising. Experiments on real noised tablet images demonstrate that the proposed method is effective both in image noise removal and image detail preserve compared with existing image denoising methods.
This paper proposed an integration de-noising method based on self-adaptive manifold filtering and text contour for ancient Chinese calligraphy tablet image. It consists of two main operations in sequence: image smoothing with a non-local means (NL-means) based self-adaptive manifold filter and isolated blocks removal based on text contour. Experiments demonstrate that the proposed method is superior to several recent published stele image de-noising techniques in terms of preserving the character structures.
The Euler number of a binary image is an important topological property for pattern recognition, image analysis, and computer vision. In the proposed algorithm, only three comparisons need to be completed for processing a bit-quad in the given image. Moreover, the proposed algorithm processes three rows simultaneously in the scanning which will reduce the number of checked pixels from 4 to 1.5 for processing each bit-quad, which will lead to an efficient processing. Experimental results demonstrated that the performance of the proposed algorithm significantly overpasses conventional Euler number computing algorithms.
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