Different camera sensors have different noise patterns, and thus an image denoising model trained on one sensor often does not generalize well to a different sensor. One plausible solution is to collect a large dataset for each sensor for training or fine-tuning, which is inevitably time-consuming. To address this cross-domain challenge, we present a novel adaptive domain learning (ADL) scheme for cross-domain RAW image denoising by utilizing existing data from different sensors (source domain) plus a small amount of data from the new sensor (target domain). The ADL training scheme automatically removes the data in the source domain that are harmful to fine-tuning a model for the target domain (some data are harmful as adding them during training lowers the performance due to domain gaps). Also, we introduce a modulation module to adopt sensor-specific information (sensor type and ISO) to understand input data for image denoising. We conduct extensive experiments on public datasets with various smartphone and DSLR cameras, which show our proposed model outperforms prior work on cross-domain image denoising, given a small amount of image data from the target domain sensor.

The lack of large-scale noisy-clean image pairs restricts supervised denoising methods' deployment in actual applications. While existing unsupervised methods are able to learn image denoising without ground-truth clean images, they either show poor performance or work under impractical settings (e.g., paired noisy images). In this paper, we present a practical unsupervised image denoising method to achieve state-of-the-art denoising performance. Our method only requires single noisy images and a noise model, which is easily accessible in practical raw image denoising. It performs two steps iteratively: (1) Constructing a noisier-noisy dataset with random noise from the noise model; (2) training a model on the noisier-noisy dataset and using the trained model to refine noisy images to obtain the targets used in the next round. We further approximate our full iterative method with a fast algorithm for more efficient training while keeping its original high performance. Experiments on real-world, synthetic, and correlated noise show that our proposed unsupervised denoising approach has superior performances over existing unsupervised methods and competitive performance with supervised methods. In addition, we argue that existing denoising datasets are of low quality and contain only a small number of scenes. To evaluate raw image denoising performance in real-world applications, we build a high-quality raw image dataset SenseNoise-500 that contains 500 real-life scenes. The dataset can serve as a strong benchmark for better evaluating raw image denoising. Code and dataset will be released11https://github.com/zhangyi-3/IDR

We present a controllable camera simulator based on deep neural networks to synthesize raw image data under different camera settings, including exposure time, ISO, and aperture. The proposed simulator includes an exposure module that utilizes the principle of modern lens designs for correcting the luminance level. It also contains a noise module using the noise level function and an aperture module with adaptive attention to simulate the side effects on noise and defocus blur. To facilitate the learning of a simulator model, we collect a dataset of the 10,000 raw images of 450 scenes with different exposure settings. Quantitative experiments and qualitative comparisons show that our approach outperforms relevant baselines in raw data synthesize on multiple cameras. Furthermore, the camera simulator enables various applications, including large-aperture enhancement, HDR, auto exposure, and data augmentation for training local feature detectors. Our work represents the first attempt to simulate a camera sensor’s behavior leveraging both the advantage of traditional raw sensor features and the power of data-driven deep learning. The code and the dataset are available at https://github.com/ken-ouyang/neural_image_simulator.

This paper investigates a new beamforming approach for interference exploitation, which has recently attracted interest as an alternative to conventional interference-avoidance beamforming for the downlink of multiple-input multiple-output systems. Contrary to existing interference exploitation approaches that focus on signal-to-noise ratio performance, we adopt an approach based on the detection region of the signal constellation. Focusing on quality of service, we then formulate the optimization for minimizing the error probability (EP) for the worst user, subject to power constraints. We do this by employing the knowledge of channel state information at the transmitter, along with all downlink users’ data that are readily available at the base station during downlink transmission. In this context, we also show that the detection-region-based beamforming and the worst user EP downlink beamforming are equivalent problems. Finally, we further propose a sum EPs approach and provide an analytic bound of average symbol error rate performance. Our simulations verify that the proposed techniques provide significantly improved performance over conventional downlink beamforming techniques.

In this paper, we study a constructive interference based cognitive radio beamforming optimization problem under perfect channel state information at the transmitter and the knowledge of data information. The beamformers are designed to minimize the worst secondary user's symbol error probability under constraints on the instantaneous total transmit power, and the power of the instantaneous interference in the primary link. The problem is formulated as a bivariate probabilistic constrained programming problem and can be solved using the barrier method. Our simulations indicate that the proposed technique offers a significantly improved performance over the conventional technique, while guaranteeing the quality of service (QoS) of primary users on an instantaneous basis, in contrast to the average QoS guarantees of conventional beamformers.

Quality of service (QoS) is commonly measured in terms of signal to interference plus noise ratio (SINR), where multiuser interference is mitigated in order to improve the performance. As opposed to conventional suppression, interference can be exploited constructively to enhance the desired signal. With the aid of channel state information (CSI) at the transmitter and data information, we study symbol-level downlink beamforming problems based on noise robustness and outage probability, respectively, subject to power constraints. We further show that an equivalence relationship between the noise robustness and outage probability symbol-level downlink beamforming problems can be obtained. Finally, we provide an analytic symbol error rate (SER) upper bound of the worst user by solving the outage probability-based problem. Our simulations demonstrate that the proposed techniques provide substantial performance improvements over conventional downlink beamforming techniques.

We consider the problem of worst-case robust beamforming for multi-group multicasting network with erroneous channel state information (CSI). In previous beamforming techniques robustness is ensured for all mismatch matrices of bounded Frobenius norm. In contrast, we present an alternative method of bounding the channel uncertainties, where we only limit the trace of the mismatch matrices. This approach leads to a problem formulation of reduced complexity as compared to the previous methods. Our goal is to minimize the total transmitted power subject to the worst-case user quality-of service (QoS) constraints. Lagrange duality is used to obtain a simple reformulation of the worst-case beamforming problem. The resulting non-convex problem can then be converted into a convex form using semidefinite relaxation (SDR) that can be solved efficiently using interior point methods. The resulting problem is a linear second-order cone programming (SOCP) problem as opposed to the quadratic SOCP problems in the previous robust approaches. Simulation results also show that the proposed method offers a significantly improved performance in terms of transmitted power.

We study the theory and algorithms of an optimal use of multidimensional signal reconstruction from multichannel acquisition by using a filter bank setup. Suppose that we have an N-channel convolution system, referred to as N analysis filters, in M dimensions. Instead of taking all the data and applying multichannel deconvolution, we first reduce the collected data set by an integer M×M uniform sampling matrix [Formula: see text], and then search for a synthesis polyphase matrix which could perfectly reconstruct any input discrete signal. First, we determine the existence of perfect reconstruction (PR) systems for a given set of finite-impulse response (FIR) analysis filters. Second, we present an efficient algorithm to find a sampling matrix with maximum sampling rate and to find a FIR PR synthesis polyphase matrix for a given set of FIR analysis filters. Finally, once a particular FIR PR synthesis polyphase matrix is found, we can characterize all FIR PR synthesis matrices, and then find an optimal one according to design criteria including robust reconstruction in the presence of noise.

We provide an analysis of the algorithms necessary for the optimal use of multidimensional signal reconstruction from multichannel acquisition. First, we provide computable conditions to test the matrix invertibility and propose algorithms to find a particular inverse. Second, we determine the existence of perfect reconstruction systems for given FIR analysis filters with some sampling matrices and some FIR synthesis polyphase matrices. Then, we present the development of an efficient algorithm designed to find a sampling matrix with maximum sampling rate and FIR synthesis polyphase matrix for given FIR analysis filters so that the system provides a perfect reconstruction. Once a particular synthesis matrix is found, we can characterize all synthesis matrices and find an optimal one according to a design criterion.

We study the invertibility of M-variate polynomial (respectively : Laurent polynomial) matrices of size N by P. Such matrices represent multidimensional systems in various settings including filter banks, multiple-input multiple-output systems, and multirate systems. The main result of this paper is to prove that when N - P ges M, then H(z) is generically invertible; whereas when N - P Lt M, then H(z) is generically noninvertible. As a result, we can have an alternative approach in design of the multidimensional systems.