Since non-blind Super Resolution (SR) fails to super-resolve Low-Resolution (LR) images degraded by arbitrary degradations, SR with the degradation model is required. However, this paper reveals that non-blind SR that is trained simply with various blur kernels exhibits comparable performance as those with the degradation model for blind SR. This result motivates us to revisit high-performance non-blind SR and extend it to blind SR with blur kernels. This paper proposes two SR networks by integrating kernel estimation and SR branches in an iterative end-to-end manner. In the first model, which is called the Kernel Conditioned Back-Projection Network (KCBPN), the low-dimensional kernel representations are estimated for conditioning the SR branch. In our second model, the Kernelized BackProjection Network (KBPN), a raw kernel is estimated and directly employed for modeling the image degradation. The estimated kernel is employed not only for back-propagating its residual but also for forward-propagating the residual to iterative stages. This forward-propagation encourages these stages to learn a variety of different features in different stages by focusing on pixels with large residuals in each stage. Experimental results validate the effectiveness of our proposed networks for kernel estimation and SR. We will release the code for this work.
Blind Image Quality Assessment Using Multi-Stream Architecture with Spatial and Channel Attention
Xi’an Eurasia University（西安欧亚学院）；Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences（中国科学院长春光学精密机械与物理研究所）；University of Engineering and Technology Lahore（拉合尔工程技术大学）
University of Engineering and Technology Lahore（拉合尔工程技术大学）
The assessment of the perceptual quality of digital images is becoming increasingly important as a result of the widespread use of digital multimedia devices. Smartphones and high-speed internet are just two examples of technologies that have multiplied the amount of multimedia content available. Thus, obtaining a representative dataset, which is required for objective quality assessment training, is a significant challenge. The Blind Image Quality Assessment Database, BIQ2021, is presented in this article. By selecting images with naturally occurring distortions and reliable labeling, the dat…
University of Michigan（密歇根大学）；Shanghai Jiao Tong University（上海交通大学）
Blind image super-resolution(SR) is a long-standing task in CV that aims to restore low-resolution images suffering from unknown and complex distortions. Recent work has largely focused on adopting more complicated degradation models to emulate real-world degradations. The resulting models have made breakthroughs in perceptual loss and yield perceptually convincing results. However, the limitation brought by current generative adversarial network structures is still significant: treating pixels equally leads to the ignorance of the image's structural features, and results in performance drawba…
Tsinghua University（清华大学）；Beijing Institute of Technology（北京理工大学）
Under low-light environment, handheld photography suffers from severe camera shake under long exposure settings. Although existing deblurring algorithms have shown promising performance on well-exposed blurry images, they still cannot cope with low-light snapshots. Sophisticated noise and saturation regions are two dominating challenges in practical low-light deblurring. In this work, we propose a novel non-blind deblurring method dubbed image and feature space Wiener deconvolution network (INFWIDE) to tackle these problems systematically. In terms of algorithm design, INFWIDE proposes a two-b…
Life-IQA: Boosting Blind Image Quality Assessment through GCN-enhanced Layer Interaction and MoE-based Feature Decoupling
College of Integrated Circuits, Shanghai Jiao Tong University（集成电路学院，上海交通大学）；College of Integrated Circuits, Beijing University of Chemical Technology（集成电路学院，北京化工大学）；ICCI and the Institute of Image Communication and Information Processing, Shanghai Jiao Tong University（ICCI和图像通信与信息处理研究所，上海交通大学）
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Tübingen（图宾根大学）
We present a neural network model approach for multi-frame blind deconvolution. The discriminative approach adopts and combines two recent techniques for image deblurring into a single neural network architecture. Our proposed hybrid-architecture combines the explicit prediction of a deconvolution filter and non-trivial averaging of Fourier coefficients in the frequency domain. In order to make full use of the information contained in all images in one burst, the proposed network embeds smaller networks, which explicitly allow the model to transfer information between images in early layers. O…
University of Bern（伯尔尼大学）；University of Maryland, College Park（马里兰大学学院市分校）
We propose to leverage denoising autoencoder networks as priors to address image restoration problems. We build on the key observation that the output of an optimal denoising autoencoder is a local mean of the true data density, and the autoencoder error (the difference between the output and input of the trained autoencoder) is a mean shift vector. We use the magnitude of this mean shift vector, that is, the distance to the local mean, as the negative log likelihood of our natural image prior. For image restoration, we maximize the likelihood using gradient descent by backpropagating the auto…
Digital watermark is a commonly used technique to protect the copyright of medias. Simultaneously, to increase the robustness of watermark, attacking technique, such as watermark removal, also gets the attention from the community. Previous watermark removal methods require to gain the watermark location from users or train a multi-task network to recover the background indiscriminately. However, when jointly learning, the network performs better on watermark detection than recovering the texture. Inspired by this observation and to erase the visible watermarks blindly, we propose a novel two-…
Blind deconvolution is a challenging problem, but in low-light it is even more difficult. Existing algorithms, both classical and deep-learning based, are not designed for this condition. When the photon shot noise is strong, conventional deconvolution methods fail because (1) the image does not have enough signal-to-noise ratio to perform the blur estimation; (2) While deep neural networks are powerful, many of them do not consider the forward process. When the noise is strong, these networks fail to simultaneously deblur and denoise; (3) While iterative schemes are known to be robust in the …
