Image undersampling and image size reduction. 数字图像表示法:图像取样和量化。图像欠采样和缩小图像尺寸。4. Image resizing and restoration: Nearest-neighbor, bilinear and bicubic interpolation technique for image restoration. 图像大小调整和修复:用于图像复原的最近邻插值、双线性插值和双三次插值技术。5. ...
This work aims to enhance the deep image prior (DIP) method’s effectiveness and efficiency by introducing a hybrid strategy that combines supervised and unsupervised approaches, termed EDIP (enhancing deep image prior). Our method incorporates roughly clean image pairs and employs two advanced ...
, downsampling pattern), the output is then compared to the target image (e.g., downsampled PAM image) in the objective function for backpropagation and optimization. Although traditional supervised DL methods require pre-training with ground truth, the DIP model requires only the target image ...
In this paper, we propose a method named drop-DIP combing Deep Image Prior (DIP) with drop-out for the first time to solve the above problems. In our method, we construct new network training pairs by performing drop-out training on the Bernoulli sampling of th...
(DIP) that is an unsupervised scan-adaptive method that leverages the network architecture as implicit regularization but can suffer from noise overfitting, and diffusion models (DMs), where the sampling procedure of a pre-trained generative model is modified to allow sampling from the measurement-...
@boun.edu.tr 2{gustav.bredell, ender.konukoglu}@vision.ee.ethz.ch Abstract Recent works show that convolutional neural network (CNN) architectures have a spectral bias towards lower frequencies, which has been leveraged for various image restoration tasks in ...
Zhe-MingLu,Shi-ZeGuo, inLossless Information Hiding in Images, 2017 1.1.2.2.2Image Restoration Images are often degraded during the data acquisition process. The degradation may involve motion blurring, information loss due to sampling, camera misfocus, quantization effects, and various sources of ...
Deep Image Prior (DIP) is currently among the most efficient unsupervised deep learning based methods for ill-posed inverse problems in imaging. This novel framework relies on the implicit regularization provided by representing images as the output of generative Convolutional Neural Network (CNN) archi...
In addition, the coarse-scale network has the following parts: convolution, max-pooling, up-sampling and linear combination. These operators are similar to the fine-scale network except the first and second convolution layers. The experimental results of this method show that the haze removed ...
(2021c) present a hybrid CNN-Transformer, named Dual-Domain Transformer (DuDoTrans), by considering the global nature of sinogram’s sampling process to better restore high-quality images. In the first step, DuDoTrans reconstructs low-quality reconstructions of sinogram via filtered back projection...