Topic
Standard test image
About: Standard test image is a research topic. Over the lifetime, 5217 publications have been published within this topic receiving 98486 citations.
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Book•
03 Oct 1988TL;DR: This chapter discusses two Dimensional Systems and Mathematical Preliminaries and their applications in Image Analysis and Computer Vision, as well as image reconstruction from Projections and image enhancement.
Abstract: Introduction. 1. Two Dimensional Systems and Mathematical Preliminaries. 2. Image Perception. 3. Image Sampling and Quantization. 4. Image Transforms. 5. Image Representation by Stochastic Models. 6. Image Enhancement. 7. Image Filtering and Restoration. 8. Image Analysis and Computer Vision. 9. Image Reconstruction From Projections. 10. Image Data Compression.
8,504 citations
Book•
01 Dec 2003
TL;DR: 1. Fundamentals of Image Processing, 2. Intensity Transformations and Spatial Filtering, and 3. Frequency Domain Processing.
Abstract: 1. Introduction. 2. Fundamentals. 3. Intensity Transformations and Spatial Filtering. 4. Frequency Domain Processing. 5. Image Restoration. 6. Color Image Processing. 7. Wavelets. 8. Image Compression. 9. Morphological Image Processing. 10. Image Segmentation. 11. Representation and Description. 12. Object Recognition.
6,306 citations
TL;DR: A generative appearance-based method for recognizing human faces under variation in lighting and viewpoint that exploits the fact that the set of images of an object in fixed pose but under all possible illumination conditions, is a convex cone in the space of images.
Abstract: We present a generative appearance-based method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a small number of training images of each face taken with different lighting directions, the shape and albedo of the face can be reconstructed. In turn, this reconstruction serves as a generative model that can be used to render (or synthesize) images of the face under novel poses and illumination conditions. The pose space is then sampled and, for each pose, the corresponding illumination cone is approximated by a low-dimensional linear subspace whose basis vectors are estimated using the generative model. Our recognition algorithm assigns to a test image the identity of the closest approximated illumination cone. Test results show that the method performs almost without error, except on the most extreme lighting directions.
5,027 citations
TL;DR: An efficient general-purpose blind/no-reference image quality assessment (IQA) algorithm using a natural scene statistics model of discrete cosine transform (DCT) coefficients, which requires minimal training and adopts a simple probabilistic model for score prediction.
Abstract: We develop an efficient general-purpose blind/no-reference image quality assessment (IQA) algorithm using a natural scene statistics (NSS) model of discrete cosine transform (DCT) coefficients. The algorithm is computationally appealing, given the availability of platforms optimized for DCT computation. The approach relies on a simple Bayesian inference model to predict image quality scores given certain extracted features. The features are based on an NSS model of the image DCT coefficients. The estimated parameters of the model are utilized to form features that are indicative of perceptual quality. These features are used in a simple Bayesian inference approach to predict quality scores. The resulting algorithm, which we name BLIINDS-II, requires minimal training and adopts a simple probabilistic model for score prediction. Given the extracted features from a test image, the quality score that maximizes the probability of the empirically determined inference model is chosen as the predicted quality score of that image. When tested on the LIVE IQA database, BLIINDS-II is shown to correlate highly with human judgments of quality, at a level that is competitive with the popular SSIM index.
1,484 citations
ETH Zurich1, University of California, Merced2, University of Hong Kong3, Seoul National University4, The Chinese University of Hong Kong5, Chinese Academy of Sciences6, KAIST7, University of Illinois at Urbana–Champaign8, Harbin Institute of Technology9, Xiamen University10, Peking University11, University of Missouri12, University of Sydney13, Beijing University of Posts and Telecommunications14, Shandong University15, Australian National University16, Sejong University17, Pennsylvania State University18, Tampere University of Technology19, Indian Institute of Technology Kharagpur20, École Polytechnique Fédérale de Lausanne21, University of Electronic Science and Technology of China22
TL;DR: This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.
Abstract: This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results. A new DIVerse 2K resolution image dataset (DIV2K) was employed. The challenge had 6 competitions divided into 2 tracks with 3 magnification factors each. Track 1 employed the standard bicubic downscaling setup, while Track 2 had unknown downscaling operators (blur kernel and decimation) but learnable through low and high res train images. Each competition had ∽100 registered participants and 20 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.
1,243 citations