13 accepted papers
Panorama video recently attracts more interest in both study and application courtesy of its immersive experience. Due to the expensive cost of capturing 360-degree panoramic videos generating desirable panorama videos by prompts is urgently required. Lately the emerging text-to-video (T2V) diffusio…
Despite the ability of text-to-image (T2I) diffusion models to generate high-quality images, transferring this ability to accurate image editing remains a challenge. In this paper, we propose a novel image editing method, DragonDiffusion, enabling Drag-style manipulation on Diffusion models. Specifi…
Despite significant advancements in video generation and editing using diffusion models, achieving accurate and localized video editing remains a substantial challenge. Additionally, most existing video editing methods primarily focus on altering visual content, with limited research dedicated to mo…
The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated strong power of learning complex structures and meaningful semantics. However, relying solely on text prompts cannot fully take advantage of the knowledge learned by the model, especially when flexible and a…
Video steganography is the art of unobtrusively concealing secret data in a cover video and then recovering the secret data through a decoding protocol at the receiver end. Although several attempts have been made, most of them are limited to low-capacity and fixed steganography. To rectify these we…
By integrating certain optimization solvers with deep neural networks, deep unfolding network (DUN) with good interpretability and high performance has attracted growing attention in compressive sensing (CS). However, existing DUNs often improve the visual quality at the price of a large number of p…
Deep neural networks (DNN) have achieved great success in image restoration. However, most DNN methods are designed as a black box, lacking transparency and interpretability. Although some methods are proposed to combine traditional optimization algorithms with DNN, they usually demand pre-defined d…
"Interactive image restoration aims to restore images by adjusting several controlling coefficients, which determine the restoration strength. Existing methods are restricted in learning the controllable functions under the supervision of known degradation types and levels. They usually suffer from…
Image steganography aims to hide secret images into a container image, where the secret is hidden from human vision and can be restored when necessary. Previous image steganography methods are limited in hiding capacity and robustness, commonly vulnerable to distortion on container images such as Ga…
Snapshot compressive imaging (SCI) aims to record three-dimensional signals via a two-dimensional camera. For the sake of building a fast and accurate SCI recovery algorithm, we incorporate the interpretability of model-based methods and the speed of learning-based ones and present a novel dense dee…
Non-local self-similarity in natural images has been verified to be an effective prior for image restoration. However, most existing deep non-local methods assign a fixed number of neighbors for each query item, neglecting the dynamics of non-local correlations. Moreover, the non-local correlations…
Image restoration techniques generally use intrinsic correlations of image signals to reduce the uncertainty of the unknown signal and estimate the latent ground truth. Local and non-local correlation are the two major kinds of correlations utilized. They are different sources of correlations reflec…
Local and non-local attentions are both effective methods in the domain of image restoration (IR). However, most existing image restoration methods use these two strategies indiscriminately, and how to make a trade-off between local and non-local attention operations has hardly been studied. Further…