Statisticians, as well as machine learning and computer vision experts, have been studying image reconstitution through denoising different domains of photography, such as textual documentation, tomographic, astronomical, and low-light photography. In this paper, we apply common inferential kernel filters in the R and python languages, as well as Adobe Lightroom's denoise filter, and compare their effectiveness in removing noise from JPEG images. We ran standard benchmark tests to evaluate each method's effectiveness for removing noise. In doing so, we also surveyed students at Elon University about their opinion of a single filtered photo from a collection of photos processed by the various filter methods. Many scientists believe that noise filters cause blurring and image quality loss so we analyzed whether or not people felt as though denoising causes any quality loss as compared to their noiseless images. Individuals assigned scores indicating the image quality of a denoised photo compared to its noiseless counterpart on a 1 to 10 scale. Survey scores are compared across filters to evaluate whether there were significant differences in image quality scores received. Benchmark scores were compared to the visual perception scores. Then, an analysis of covariance test was run to identify whether or not survey training scores explained any unplanned variation in visual scores assigned by students across the filter methods.
The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters. We describe a generalization of RAISR, which we name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable edge-adaptive filtering framework that is general, simple, computationally efficient, and useful for a wide range of problems in computational photography. We show applications to operations which may appear in a camera pipeline including denoising, demosaicing, and stylization.
Many photography websites such as Flickr, 500px, Unsplash, and Adobe Behance are used by amateur and professional photography enthusiasts. Unlike content-based image search, such users of photography websites are not just looking for photos with certain content, but more generally for photos with a certain photographic "aesthetic". In this context, we explore personalized photo recommendation and propose two aesthetic feature extraction methods based on (i) color space and (ii) deep style transfer embeddings. Using a dataset from 500px, we evaluate how these features can be best leveraged by c…
Beijing National Research Center for Information Science and Technology（北京国家信息科学与技术研究中心）；Institute for Brain and Cognitive Sciences, Tsinghua University（清华大学脑与认知科学研究院）；Tsinghua University（清华大学）
Imaging and perception in photon-limited scenarios is necessary for various applications, e.g., night surveillance or photography, high-speed photography, and autonomous driving. In these cases, cameras suffer from low signal-to-noise ratio, which degrades the image quality severely and poses challenges for downstream high-level vision tasks like object detection and recognition. Data-driven methods have achieved enormous success in both image restoration and high-level vision tasks. However, the lack of high-quality benchmark dataset with task-specific accurate annotations for photon-limited …
The bilateral filter is an edge-preserving smoother that has diverse applications in image processing, computer vision, computer graphics, and computational photography. The filter uses a spatial kernel along with a range kernel to perform edge-preserving smoothing. In this paper, we consider the Gaussian bilateral filter where both the kernels are Gaussian. A direct implementation of the Gaussian bilateral filter requires $O(σ_s^2)$ operations per pixel, where $σ_s$ is the standard deviation of the spatial Gaussian. In fact, it is well-known that the direct implementation is slow in practice.…
The bilateral filter is a versatile non-linear filter that has found diverse applications in image processing, computer vision, computer graphics, and computational photography. A widely-used form of the filter is the Gaussian bilateral filter in which both the spatial and range kernels are Gaussian. A direct implementation of this filter requires $O(σ^2)$ operations per pixel, where $σ$ is the standard deviation of the spatial Gaussian. In this paper, we propose an accurate approximation algorithm that can cut down the computational complexity to $O(1)$ per pixel for any arbitrary $σ$ (consta…
Integrity Monitoring of 3D Object Detection in Automated Driving Systems using Raw Activation Patterns and Spatial Filtering
WMG, University of Warwick（华威大学WMG学院）；School of Electronics, Electrical Engineering and Computer Science (EEECS), Queen’s University of Belfast（贝尔法斯特女王大学电子、电气工程与计算机科学学院）
IDF: Iterative Dynamic Filtering Networks for Generalizable Image Denoising
Department of Computer Science, Hanyang University（翰阳大学计算机科学系）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）
Indian Institute of Technology, Hyderabad（印度理工学院海得拉巴分校）；Indian Institute of Science（印度科学学院）
The bilateral filter has diverse applications in image processing, computer vision, and computational photography. In particular, this non-linear filter is quite effective in denoising images corrupted with additive Gaussian noise. The filter, however, is known to perform poorly at large noise levels. Several adaptations of the filter have been proposed in the literature to address this shortcoming, but often at an added computational cost. In this paper, we report a simple yet effective modification that improves the denoising performance of the bilateral filter at almost no additional cost. …
In photography, low depth of field (DOF) is an important technique to emphasize the object of interest (OOI) within an image. Thus, low DOF images are widely used in the application area of macro, portrait or sports photography. When viewing a low DOF image, the viewer implicitly concentrates on the regions that are sharper regions of the image and thus segments the image into regions of interest and non regions of interest which has a major impact on the perception of the image. Thus, a robust algorithm for the fully automatic detection of the OOI in low DOF images provides valuable informati…
Bosch Center for Artificial Intelligence（博世人工智能中心）；University of Bonn（波恩大学）
The performance of autonomous systems heavily relies on their ability to generate a robust representation of the environment. Deep neural networks have greatly improved vision-based perception systems but still fail in challenging situations, e.g. sensor outages or heavy weather. These failures are often introduced by data-inherent perturbations, which significantly reduce the information provided to the perception system. We propose a functionally modularized temporal filter, which stabilizes an abstract feature representation of a single-frame segmentation model using information of previous…
In the absence of a pure noise-free image it is hard to define what noise is, in any original noisy image, and as a consequence also where it is, and in what amount. In fact, the definition of noise depends largely on our own aim in the whole image analysis process, and (perhaps more important) in our self-perception of noise. For instance, when we perceive noise as disconnected and small it is normal to use MM-ASF filters to treat it. There is two evidences of this. First, in many instances there is no ideal and pure noise-free image to compare our filtering process (nothing but our self-perc…
Universitat Pompeu Fabra（庞培法布拉大学）；École Polytechnique（巴黎综合理工学院）；CNRS（法国国家科学研究中心）；Université Paris-Sud（巴黎南大学）；Fondation Asile des Aveugles（盲人庇护基金会）；CHUV（洛桑大学医院）；Centrale Supelec（巴黎中央理工-高等电力学院）
We consider a differential model describing neuro-physiological contrast perception phenomena induced by surrounding orientations. The mathematical formulation relies on a cortical-inspired modelling [10] largely used over the last years to describe neuron interactions in the primary visual cortex (V1) and applied to several image processing problems [12,19,13]. Our model connects to Wilson-Cowan-type equations [23] and it is analogous to the one used in [3,2,14] to describe assimilation and contrast phenomena, the main novelty being its explicit dependence on local image orientation. To confi…
Robust environment perception is essential for decision-making on robots operating in complex domains. Principled treatment of uncertainty sources in a robot's observation model is necessary for accurate mapping and object detection. This is important not only for low-level observations (e.g., accelerometer data), but for high-level observations such as semantic object labels as well. This paper presents an approach for filtering sequences of object classification probabilities using online modeling of the noise characteristics of the classifier outputs. A hierarchical Bayesian approach is use…
Neuromorphic vision is a rapidly growing field with numerous applications in the perception systems of autonomous vehicles. Unfortunately, due to the sensors working principle, there is a significant amount of noise in the event stream. In this paper we present a novel algorithm based on an IIR filter matrix for filtering this type of noise and a hardware architecture that allows its acceleration using an SoC FPGA. Our method has a very good filtering efficiency for uncorrelated noise - over 99% of noisy events are removed. It has been tested for several event data sets with added random noise…
Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；University of Science and Technology of China（中国科学技术大学）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）
Multiscale Sliced Wasserstein Distances as Perceptual Color Difference Measures
City University of Hong Kong（香港城市大学）；Shenzhen Research Institute, City University of Hong Kong（香港城市大学深圳研究院）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；Guangdong OPPO Mobile Telecommunications Corp., Ltd.（广东欧珀移动通信有限公司）；Jiangxi University of Finance and Economics（江西财经大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））
College of Science and Engineering, Flinders University（弗林德斯大学科学与工程学院）
Why does our visual system fail to reconstruct reality, when we look at certain patterns? Where do Geometrical illusions start to emerge in the visual pathway? How far should we take computational models of vision with the same visual ability to detect illusions as we do? This study addresses these questions, by focusing on a specific underlying neural mechanism involved in our visual experiences that affects our final perception. Among many types of visual illusion, Geometrical and, in particular, Tilt Illusions are rather important, being characterized by misperception of geometric patterns …
Efficient Dual-domain Image Dehazing with Haze Prior Perception
Institute of Intelligent Information Processing, Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Modern Communications and Information Processing, Shenzhen University（智能信息处理研究院）；College of Engineering and Computer Science, Australian National University（工程与计算机科学学院）
EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within Transformer
Hefei University of Technology (HFUT)（合肥工业大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；Anhui Zhonghuitong Technology Co., Ltd.（安徽中汇通科技有限公司）
Patch Matters: Training-free Fine-grained Image Caption Enhancement via Local Perception
School of Future Technology, South China University of Technology（华南理工大学未来技术学院）；College of Science, China Agricultural University（中国农业大学理学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Engineering, Westlake University（西湖大学工程学院）
ALEN: A Dual-Approach for Uniform and Non-Uniform Low-Light Image Enhancement
School of Computer Science and Technology, Zhejiang Gongshang University（计算机科学与技术学院，浙江工商大学）；Depto. de Ingeniería Electro-fotónica, Universidad de Guadalajara（电子光子工程系，瓜达拉哈拉大学）
New York University（纽约大学）；NYU Langone Health（纽约大学朗格尼医学中心）；Warsaw University of Technology（华沙理工大学）
Deep neural networks (DNNs) show promise in image-based medical diagnosis, but cannot be fully trusted since their performance can be severely degraded by dataset shifts to which human perception remains invariant. If we can better understand the differences between human and machine perception, we can potentially characterize and mitigate this effect. We therefore propose a framework for comparing human and machine perception in medical diagnosis. The two are compared with respect to their sensitivity to the removal of clinically meaningful information, and to the regions of an image deemed m…
Scale Contrastive Learning with Selective Attentions for Blind Image Quality Assessment
School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；School of Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Xiamen University（多媒体可信感知与高效计算重点实验室，厦门大学）；Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University（北京信息科学与技术国家研究中心（BNRist），清华大学）
A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement
School of Information Engineering, Xi’an Eurasia University, Xi’an, 710065, Shaanxi, China（西安欧亚大学信息工程学院）；Department of Electrical Engineering, University of Engineering and Technology Lahore（拉合尔工程与技术大学电气工程系）；Department of Computer Science, University of Engineering and Technology Lahore（拉合尔工程与技术大学计算机科学系）
