A Task-Guided, Implicitly-Searched and Meta-Initialized Deep Model for Image Fusion.

Image fusion plays a key role in a variety of multi-sensor-based vision systems, especially for enhancing visual quality and/or extracting aggregated features for perception. However, most existing methods just consider image fusion as an individual task, thus ignoring its underlying relationship with these downstream vision problems. Furthermore, designing proper fusion architectures often requires huge engineering labor.

Optimization-Inspired Learning With Architecture Augmentations and Control Mechanisms for Low-Level Vision.

In recent years, there has been a growing interest in combining learnable modules with numerical optimization to solve low-level vision tasks. However, most existing approaches focus on designing specialized schemes to generate image/feature propagation. There is a lack of unified consideration to construct propagative modules, provide theoretical analysis tools, and design effective learning mechanisms.

Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions.

Deformable image registration plays a critical role in various tasks of medical image analysis. A successful registration algorithm, either derived from conventional energy optimization or deep networks, requires tremendous efforts from computer experts to well design registration energy or to carefully tune network architectures with respect to medical data available for a given registration task/scenario. This paper proposes an automated learning registration algorithm (AutoReg) that cooperatively optimizes both architectures and their corresponding training objectives, enabling non-computer experts to conveniently find off-the-shelf registration algorithms for various registration scenarios.

Characteristic Mapping for Ellipse Detection Acceleration.

It is challenging to characterize the intrinsic geometry of high-degree algebraic curves with lower-degree algebraic curves. The reduction in the curve's degree implies lower computation costs, which is crucial for various practical computer vision systems. In this paper, we develop a characteristic mapping (CM) to recursively degenerate 3n points on a planar curve of n th order to 3(n-1) points on a curve of (n-1) th order.

A Parametric Design Method for Engraving Patterns on Thin Shells.

Designing thin-shell structures that are diverse, lightweight, and physically viable is a challenging task for traditional heuristic methods. To address this challenge, we present a novel parametric design framework for engraving regular, irregular, and customized patterns on thin-shell structures. Our method optimizes pattern parameters such as size and orientation, to ensure structural stiffness while minimizing material consumption.

Learning With Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision.

Images captured from low-light scenes often suffer from severe degradations, including low visibility, color casts, intensive noises, etc. These factors not only degrade image qualities, but also affect the performance of downstream Low-Light Vision (LLV) applications. A variety of deep networks have been proposed to enhance the visual quality of low-light images.

Learning Deformable Image Registration From Optimization: Perspective, Modules, Bilevel Training and Beyond.

Conventional deformable registration methods aim at solving an optimization model carefully designed on image pairs and their computational costs are exceptionally high. In contrast, recent deep learning-based approaches can provide fast deformation estimation. These heuristic network architectures are fully data-driven and thus lack explicit geometric constraints which are indispensable to generate plausible deformations, e.

Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement.

Enhancing the quality of low-light (LOL) images plays a very important role in many image processing and multimedia applications. In recent years, a variety of deep learning techniques have been developed to address this challenging task. A typical framework is to simultaneously estimate the illumination and reflectance, but they disregard the scene-level contextual information encapsulated in feature spaces, causing many unfavorable outcomes, e.

Underexposed Image Correction via Hybrid Priors Navigated Deep Propagation.

Enhancing visual quality for underexposed images is an extensively concerning task that plays an important role in various areas of multimedia and computer vision. Most existing methods often fail to generate high-quality results with appropriate luminance and abundant details. To address these issues, we develop a novel framework, integrating both knowledge from physical principles and implicit distributions from data to address underexposed image correction.

A Bilevel Integrated Model With Data-Driven Layer Ensemble for Multi-Modality Image Fusion.

Image fusion plays a critical role in a variety of vision and learning applications. Current fusion approaches are designed to characterize source images, focusing on a certain type of fusion task while limited in a wide scenario. Moreover, other fusion strategies (i.

Efficient Representation and Optimization for TPMS-Based Porous Structures.

In this approach, we present an efficient topology and geometry optimization of triply periodic minimal surfaces (TPMS) based porous shell structures, which can be represented, analyzed, optimized and stored directly using functions. The proposed framework is directly executed on functions instead of remeshing (tetrahedral/hexahedral), and this framework substantially improves the controllability and efficiency. Specifically, a valid TPMS-based porous shell structure is first constructed by function expressions.

Location-Aware and Regularization-Adaptive Correlation Filters for Robust Visual Tracking.

Correlation filter (CF) has recently been widely used for visual tracking. The estimation of the search window and the filter-learning strategies is the key component of the CF trackers. Nevertheless, prevalent CF models separately address these issues in heuristic manners.

A Deep Framework Assembling Principled Modules for CS-MRI: Unrolling Perspective, Convergence Behaviors, and Practical Modeling.

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) significantly accelerates MR acquisition at a sampling rate much lower than the Nyquist criterion. A major challenge for CS-MRI lies in solving the severely ill-posed inverse problem to reconstruct aliasing-free MR images from the sparse k -space data. Conventional methods typically optimize an energy function, producing restoration of high quality, but their iterative numerical solvers unavoidably bring extremely large time consumption.

Novelty Detection and Online Learning for Chunk Data Streams.

Datastream analysis aims at extracting discriminative information for classification from continuously incoming samples. It is extremely challenging to detect novel data while incrementally updating the model efficiently and stably, especially for high-dimensional and/or large-scale data streams. This paper proposes an efficient framework for novelty detection and incremental learning for unlabeled chunk data streams.

Knowledge-Driven Deep Unrolling for Robust Image Layer Separation.

Single-image layer separation targets to decompose the observed image into two independent components in terms of different application demands. It is known that many vision and multimedia applications can be (re)formulated as a separation problem. Due to the fundamentally ill-posed natural of these separations, existing methods are inclined to investigate model priors on the separated components elaborately.

On the Convergence of Learning-Based Iterative Methods for Nonconvex Inverse Problems.

Numerous tasks at the core of statistics, learning and vision areas are specific cases of ill-posed inverse problems. Recently, learning-based (e.g.

Deep Proximal Unrolling: Algorithmic Framework, Convergence Analysis and Applications.

Deep learning models have gained great success in many real-world applications. However, most existing networks are typically designed in heuristic manners, thus these approaches lack of rigorous mathematical derivations and clear interpretations. Several recent studies try to build deep models by unrolling a particular optimization model that involves task information.

Learning Aggregated Transmission Propagation Networks for Haze Removal and Beyond.

Single-image dehazing is an important low-level vision task with many applications. Early studies have investigated different kinds of visual priors to address this problem. However, they may fail when their assumptions are not valid on specific images.

Designing a stable feedback control system for blind image deconvolution.

Blind image deconvolution is one of the main low-level vision problems with wide applications. Many previous works manually design regularization to simultaneously estimate the latent sharp image and the blur kernel under maximum a posterior framework. However, it has been demonstrated that such joint estimation strategies may lead to the undesired trivial solution.

A Fast Ellipse Detector Using Projective Invariant Pruning.

Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis, where the detection time is always a critical issue. Existing methods are hardly applicable to these real-time scenarios of limited hardware resource due to the huge number of fragment candidates (edges or arcs) for fitting ellipse equations. In this paper, we present a fast algorithm detecting ellipses with high accuracy.

Adaptive low-rank subspace learning with online optimization for robust visual tracking.

In recent years, sparse and low-rank models have been widely used to formulate appearance subspace for visual tracking. However, most existing methods only consider the sparsity or low-rankness of the coefficients, which is not sufficient enough for appearance subspace learning on complex video sequences. Moreover, as both the low-rank and the column sparse measures are tightly related to all the samples in the sequences, it is challenging to incrementally solve optimization problems with both nuclear norm and column sparse norm on sequentially obtained video data.

Learning to Diffuse: A New Perspective to Design PDEs for Visual Analysis.

Partial differential equations (PDEs) have been used to formulate image processing for several decades. Generally, a PDE system consists of two components: the governing equation and the boundary condition. In most previous work, both of them are generally designed by people using mathematical skills.

Multi-layer sparse representation for weighted LBP-patches based facial expression recognition.

In this paper, a novel facial expression recognition method based on sparse representation is proposed. Most contemporary facial expression recognition systems suffer from limited ability to handle image nuisances such as low resolution and noise. Especially for low intensity expression, most of the existing training methods have quite low recognition rates.

Fiducial facial point extraction using a novel projective invariant.

Automatic extraction of fiducial facial points is one of the key steps to face tracking, recognition, and animation.Great facial variations, especially pose or viewpoint changes,typically degrade the performance of classical methods. Recent learning or regression-based approaches highly rely on the availability of a training set that covers facial variations as wide as possible.

New insights into diversification of hyper-heuristics.

There has been a growing research trend of applying hyper-heuristics for problem solving, due to their ability of balancing the intensification and the diversification with low level heuristics. Traditionally, the diversification mechanism is mostly realized by perturbing the incumbent solutions to escape from local optima. In this paper, we report our attempt toward providing a new diversification mechanism, which is based on the concept of instance perturbation.