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In this work we present Low-rank Deconvolution, a powerful framework for low-level feature-map learning for efficient signal representation with application to signal recovery. Its formulation in multi-linear algebra inherits properties from convolutional sparse coding and low-rank approximation methods as in this setting signals are decomposed in a set of filters convolved with a set of low-rank tensors. We show its advantages by learning compressed video representations and solving image in-painting problems.

Audio-visual learning helps to comprehensively understand the world by fusing practical information from multiple modalities. However, recent studies show that the imbalanced optimization of uni-modal encoders in a joint-learning model is a bottleneck to enhancing the model`s performance. We further find that the up-to-date imbalance-mitigating methods fail on some audio-visual fine-grained tasks, which have a higher demand for distinguishable feature distribution.

Plug & Play methods combine proximal algorithms with denoiser priors to solve inverse problems. These methods rely on the computability of the proximal operator of the data fidelity term. In this paper, we propose a Plug & Play framework based on linearized ADMM that allows us to bypass the computation of intractable proximal operators. We demonstrate the convergence of the algorithm and provide results on restoration tasks such as super-resolution and deblurring with non-uniform blur.

Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from a noisy image only is a challenging task. Here, we study the case when paired noisy and noise-free samples are accessible. No method is currently available to exploit the noise-free information, which may help to achieve more accurate estimations. To fill this gap, we derive a novel, cumulant-based, approach for Poisson-Gaussian noise modeling from paired image samples.

We recently proposed a color constancy method based on the observations that the human visual system might be "discounting the illuminant" by using space-average color and the highest luminance patches. Based on these observations, our algorithm relies on two assumptions: (i) there are several bright pixels in the scene, and (ii) the world is gray, on average. The main idea of the algorithm is to estimate the illuminant by finding the deviation of the brightest pixels from the gray value. During experiments, we observed that some pixels decrease the performance of the method.

High-performance video frame interpolation is challenging for complex scenes with diverse motion and occlusion characteristics. Existing methods, deploying off-the-shelf flow estimators to acquire initial characterizations refined by multiple subsequent models, often require heavy network architectures that are not practical for resource constrained systems. We investigate the unary potentials of the characterizations to improve efficiency.

Recent technological advances in design and processing speed have successfully demonstrated a new snapshot mosaic imaging sensor architecture (SSI), allowing miniaturized platforms to efficiently acquire the spatio-spectral content of the dynamic scenes from a single exposure. However, SSI systems have a fundamental trade-off between spatial and spectral resolution because they associate each pixel with a specific spectral band.

In the age of social media, posting attractive mugshots is commonplace, leading to an urgent need for automatic facial beautification techniques. To better meet the esthetic preferences of users, we devise a customized automatic face beautification task that can retouch the face adaptively to match the user-entered target score whilst preserving the ID information as much as possible. To accomplish this task, we propose a Human Esthetics Guided StyleGAN Inversion method to retouch each face in the embedding space using StyleGAN inversion.