Image Super-Resolution via Residual Blended Attention Generative Adversarial Network with Dual Discriminators

When reviewing codes, it seems that there's something wrong with the training codes that we did not notice, this article is temporarily withdrawn and will be modified and resubmitted after codes reviewing.

Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis

In clinical diagnosis, doctors are able to see biological tissues and early lesions more clearly with the assistance of high-resolution(HR) medical images, which is of vital significance for improving diagnosis accuracy. In order to address the issue that medical images would suffer from severe blurring caused by lack of high-frequency details, this paper develops a novel image super-resolution(SR) algorithm called SR-DBAN via dense neural network and blended attention mechanism. Specifically, a novel blended attention block is proposed and introduced to dense neural network(DenseNet), so that the neural network can concentrate more attention to the regions and channels with sufficient high-frequency details adaptively. In the framework of SR-DBAN, batch normalization layers in the original DenseNet are removed to avoid loss of high-frequency texture details, final HR images are obtained by deconvolution at the very end of the network. Furthermore, inspired by the impressive performance of generative adversarial network, this paper develops a novel image SR algorithm called SR-DBAGAN via dense blended attention generative adversarial network. SR-DBAGAN consists a generator and a discriminator, the generator uses our proposed SR-DBAN to generate HR images and try to fool the discriminator while the discriminator is designed based on Wasserstein GAN(WGAN) to discriminate. We deployed our algorithms on blurry prostate MRI images, and experimental results showed that our proposed algorithms have generated considerable sharpness and texture details and have a significant improvement on the peak signal-to-noise ratio(PSNR) and structural similarity index(SSIM), respectively, compared with mainstream interpolation-based and deep learning-based image SR algorithms, which fully proves the effectiveness and superiority of our proposed algorithms.

Medical image super-resolution method based on dense blended attention network

In order to address the issue that medical image would suffer from severe blurring caused by the lack of high-frequency details in the process of image super-resolution reconstruction, a novel medical image super-resolution method based on dense neural network and blended attention mechanism is proposed. The proposed method adds blended attention blocks to dense neural network(DenseNet), so that the neural network can concentrate more attention to the regions and channels with sufficient high-frequency details. Batch normalization layers are removed to avoid loss of high-frequency texture details. Final obtained high resolution medical image are obtained using deconvolutional layers at the very end of the network as up-sampling operators. Experimental results show that the proposed method has an improvement of 0.05db to 11.25dB and 0.6% to 14.04% on the peak signal-to-noise ratio(PSNR) metric and structural similarity index(SSIM) metric, respectively, compared with the mainstream image super-resolution methods. This work provides a new idea for theoretical studies of medical image super-resolution reconstruction.