Perception-Distortion Balanced Super-Resolution: A Multi-Objective Optimization Perspective.

High perceptual quality and low distortion degree are two important goals in image restoration tasks such as super-resolution (SR). Most of the existing SR methods aim to achieve these goals by minimizing the corresponding yet conflicting losses, such as the l loss and the adversarial loss. Unfortunately, the commonly used gradient-based optimizers, such as Adam, are hard to balance these objectives due to the opposite gradient decent directions of the contradictory losses.

Deep Variational Network Toward Blind Image Restoration.

Blind image restoration (IR) is a common yet challenging problem in computer vision. Classical model-based methods and recent deep learning (DL)-based methods represent two different methodologies for this problem, each with their own merits and drawbacks. In this paper, we propose a novel blind image restoration method, aiming to integrate both the advantages of them.

Deep-learning-augmented microscopy for super-resolution imaging of nanoparticles.

Conventional optical microscopes generally provide blurry and indistinguishable images for subwavelength nanostructures. However, a wealth of intensity and phase information is hidden in the corresponding diffraction-limited optical patterns and can be used for the recognition of structural features, such as size, shape, and spatial arrangement. Here, we apply a deep-learning framework to improve the spatial resolution of optical imaging for metal nanostructures with regular shapes yet varied arrangement.

Weight Decay With Tailored Adam on Scale-Invariant Weights for Better Generalization.

Weight decay (WD) is a fundamental and practical regularization technique in improving generalization of current deep learning models. However, it is observed that the WD does not work effectively for an adaptive optimization algorithm (such as Adam), as it works for SGD. Specifically, the solution found by Adam with the WD often generalizes unsatisfactorily.

Fast Multi-Scale Structural Patch Decomposition for Multi-Exposure Image Fusion.

Exposure bracketing is crucial to high dynamic range imaging, but it is prone to halos for static scenes and ghosting artifacts for dynamic scenes. The recently proposed structural patch decomposition for multi-exposure fusion (SPD-MEF) has achieved reliable performance in deghosting, but suffers from visible halo artifacts and is computationally expensive. In addition, its relationship to other MEF methods is unclear.

Robust Multiview Subspace Learning With Nonindependently and Nonidentically Distributed Complex Noise.

Multiview Subspace Learning (MSL), which aims at obtaining a low-dimensional latent subspace from multiview data, has been widely used in practical applications. Most recent MSL approaches, however, only assume a simple independent identically distributed (i.i.

Foreground Gating and Background Refining Network for Surveillance Object Detection.

Detecting objects in surveillance videos is an important problem due to its wide applications in traffic control and public security. Existing methods tend to face performance degradation because of false positive or misalignment problems. We propose a novel framework, namely, Foreground Gating and Background Refining Network (FG-BR Net), for surveillance object detection (SOD).

Panoramic Background Image Generation for PTZ Cameras.

Being able to cover a wide range of views, pan-tilt-zoom (PTZ) cameras have been widely deployed in visual surveillance systems. To achieve a global-view perception of a surveillance scene, it is necessary to generate its panoramic background image, which can be used for the subsequent applications such as road segmentation, active tracking, and so on. However, few works have been reported on this problem, partially due to the lack of benchmark dataset and the high complexity of panoramic image generation of PTZ cameras.

Group Maximum Differentiation Competition: Model Comparison with Few Samples.

In many science and engineering fields that require computational models to predict certain physical quantities, we are often faced with the selection of the best model under the constraint that only a small sample set can be physically measured. One such example is the prediction of human perception of visual quality, where sample images live in a high dimensional space with enormous content variations. We propose a new methodology for model comparison named group maximum differentiation (gMAD) competition.

Extreme Learning Machine With Affine Transformation Inputs in an Activation Function.

The extreme learning machine (ELM) has attracted much attention over the past decade due to its fast learning speed and convincing generalization performance. However, there still remains a practical issue to be approached when applying the ELM: the randomly generated hidden node parameters without tuning can lead to the hidden node outputs being nonuniformly distributed, thus giving rise to poor generalization performance. To address this deficiency, a novel activation function with an affine transformation (AT) on its input is introduced into the ELM, which leads to an improved ELM algorithm that is referred to as an AT-ELM in this paper.

Robust Online Matrix Factorization for Dynamic Background Subtraction.

We propose an effective online background subtraction method, which can be robustly applied to practical videos that have variations in both foreground and background. Different from previous methods which often model the foreground as Gaussian or Laplacian distributions, we model the foreground for each frame with a specific mixture of Gaussians (MoG) distribution, which is updated online frame by frame. Particularly, our MoG model in each frame is regularized by the learned foreground/background knowledge in previous frames.

Robust Multi-Exposure Image Fusion: A Structural Patch Decomposition Approach.

We propose a simple yet effective structural patch decomposition approach for multi-exposure image fusion (MEF) that is robust to ghosting effect. We decompose an image patch into three conceptually independent components: signal strength, signal structure, and mean intensity. Upon fusing these three components separately, we reconstruct a desired patch and place it back into the fused image.

Waterloo Exploration Database: New Challenges for Image Quality Assessment Models.

The great content diversity of real-world digital images poses a grand challenge to image quality assessment (IQA) models, which are traditionally designed and validated on a handful of commonly used IQA databases with very limited content variation. To test the generalization capability and to facilitate the wide usage of IQA techniques in real-world applications, we establish a large-scale database named the Waterloo Exploration Database, which in its current state contains 4744 pristine natural images and 94 880 distorted images created from them. Instead of collecting the mean opinion score for each image via subjective testing, which is extremely difficult if not impossible, we present three alternative test criteria to evaluate the performance of IQA models, namely, the pristine/distorted image discriminability test, the listwise ranking consistency test, and the pairwise preference consistency test (P-test).