利用可逆神经网络与误差扩散从电导率图重建气泡分布的学习方法

LINN: Lifting Inspired Invertible Neural Network for Image Denoising

In this paper, we propose an invertible neural network for image denoising (DnINN) inspired by the transform-based denoising framework. The proposed DnINN consists of an invertible neural network called LINN whose architecture is inspired by the lifting scheme in wavelet theory and a sparsity-driven denoising network which is used to remove noise from the transform coefficients. The denoising operation is performed with a single soft-thresholding operation or with a learned iterative shrinkage thresholding network. The forward pass of LINN produces an over-complete representation which is more…

Invertible Residual Network with Regularization for Effective Medical Image Segmentation

Deep Convolutional Neural Networks (CNNs) i.e. Residual Networks (ResNets) have been used successfully for many computer vision tasks, but are difficult to scale to 3D volumetric medical data. Memory is increasingly often the bottleneck when training 3D Convolutional Neural Networks (CNNs). Recently, invertible neural networks have been applied to significantly reduce activation memory footprint when training neural networks with backpropagation thanks to the invertible functions that allow retrieving its input from its output without storing intermediate activations in memory to perform the b…

Invertible Rescaling Network and Its Extensions

Image rescaling is a commonly used bidirectional operation, which first downscales high-resolution images to fit various display screens or to be storage- and bandwidth-friendly, and afterward upscales the corresponding low-resolution images to recover the original resolution or the details in the zoom-in images. However, the non-injective downscaling mapping discards high-frequency contents, leading to the ill-posed problem for the inverse restoration task. This can be abstracted as a general image degradation-restoration problem with information loss. In this work, we propose a novel inverti…

基于可逆神经变换和隐式条件的高保真视频压缩

High-Fidelity Video Compression based on Invertible Neural Transform and Implicit Conditioning

提出InnVC，一种基于可逆神经网络和隐式条件场的视频编解码器，通过保持可逆主变换路径并解耦相关内容和细节，在高质量区域实现显著性能提升。

Subsampled Fourier Ptychography using Pretrained Invertible and Untrained Network Priors

Recently pretrained generative models have shown promising results for subsampled Fourier Ptychography (FP) in terms of quality of reconstruction for extremely low sampling rate and high noise. However, one of the significant drawbacks of these pretrained generative priors is their limited representation capabilities. Moreover, training these generative models requires access to a large number of fully-observed clean samples of a particular class of images like faces or digits that is prohibitive to obtain in the context of FP. In this paper, we propose to leverage the power of pretrained inve…

TVRN：用于压缩感知的可逆神经网络时间视频重采样

TVRN: Invertible Neural Networks for Compression-Aware Temporal Video Rescaling

本文提出TVRN框架，通过可逆架构和学习到的排名策略，解决压缩感知下的时间视频重采样问题，提升重建质量。

WINNet: Wavelet-inspired Invertible Network for Image Denoising

Image denoising aims to restore a clean image from an observed noisy image. The model-based image denoising approaches can achieve good generalization ability over different noise levels and are with high interpretability. Learning-based approaches are able to achieve better results, but usually with weaker generalization ability and interpretability. In this paper, we propose a wavelet-inspired invertible network (WINNet) to combine the merits of the wavelet-based approaches and learningbased approaches. The proposed WINNet consists of K-scale of lifting inspired invertible neural networks (L…

以秘密-隐写图像的差异性为设计轴：基于扩散模型的可逆无载体图像隐写术

Secret-Stego Dissimilarity as a Design Axis: Invertible Coverless Image Steganography with Diffusion Models

该研究提出InvCISD可逆扩散框架，耦合秘密与参考图像潜在表示，降低秘密-隐写图像视觉相似性，提升隐写质量，为CIS抗隐写分析研究提供方向。

Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling

High-resolution (HR) images are usually downscaled to low-resolution (LR) ones for better display and afterward upscaled back to the original size to recover details. Recent work in image rescaling formulates downscaling and upscaling as a unified task and learns a bijective mapping between HR and LR via invertible networks. However, in real-world applications (e.g., social media), most images are compressed for transmission. Lossy compression will lead to irreversible information loss on LR images, hence damaging the inverse upscaling procedure and degrading the reconstruction accuracy. In th…

用于学习图像压缩的近似可逆神经网络

Approximately Invertible Neural Network for Learned Image Compression

本文针对学习图像压缩中分析变换与合成变换独立设计导致的高质量压缩不可靠问题，提出近似可逆神经网络框架A-INN，并结合渐进去噪、级联特征恢复和频率增强模块有效降低量化噪声与高频信息损失，实验表明其性能优于现有方法。