There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method.

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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

On robust estimation of the location parameter

This paper proposes an adaptive color-guided autoregressive (AR) model for high quality depth recovery from low quality measurements captured by depth cameras. We observe and verify that the AR model tightly fits depth maps of generic scenes. The depth recovery task is formulated into a minimization of AR prediction errors subject to measurement consistency. The AR predictor for each pixel is constructed according to both the local correlation in the initial depth map and the nonlocal similarity in the accompanied high quality color image. We analyze the stability of our method from a linear system point of view, and design a parameter adaptation scheme to achieve stable and accurate depth recovery. Quantitative and qualitative evaluation compared with ten state-of-the-art schemes show the effectiveness and superiority of our method. Being able to handle various types of depth degradations, the proposed method is versatile for mainstream depth sensors, time-of-flight camera, and Kinect, as demonstrated by experiments on real systems.

Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model

This paper proposes a new super-resolution (SR) scheme for landmark images by retrieving correlated web images. Using correlated web images significantly improves the exemplar-based SR. Given a low-resolution (LR) image, we extract local descriptors from its up-sampled version and bundle the descriptors according to their spatial relationship to retrieve correlated high-resolution (HR) images from the web. Though similar in content, the retrieved images are usually taken with different illumination, focal lengths, and shot perspectives, resulting in uncertainty for the HR detail approximation. To solve this problem, we first propose aligning these images to the up-sampled LR image through a global registration, which identifies the corresponding regions in these images and reduces the mismatching. Second, we propose a structure-aware matching criterion and adaptive block sizes to improve the mapping accuracy between LR and HR patches. Finally, these matched HR patches are blended together by solving an energy minimization problem to recover the desired HR image. Experimental results demonstrate that our SR scheme achieves significant improvement compared with four state-of-the-art schemes in terms of both subjective and objective qualities.

Landmark Image Super-Resolution by Retrieving Web Images

Current image coding schemes make it hard to utilize external images for compression even if highly correlated images can be found in the cloud. To solve this problem, we propose a method of cloud-based image coding that is different from current image coding even on the ground. It no longer compresses images pixel by pixel and instead tries to describe images and reconstruct them from a large-scale image database via the descriptions. First, we describe an input image based on its down-sampled version and local feature descriptors. The descriptors are used to retrieve highly correlated images in the cloud and identify corresponding patches. The down-sampled image serves as a target to stitch retrieved image patches together. Second, the down-sampled image is compressed using current image coding. The feature vectors of local descriptors are predicted by the corresponding vectors extracted in the decoded down-sampled image. The predicted residual vectors are compressed by transform, quantization, and entropy coding. The experimental results show that the visual quality of reconstructed images is significantly better than that of intra-frame coding in HEVC and JPEG at thousands to one compression .

Cloud-Based Image Coding for Mobile Devices—Toward Thousands to One Compression

Cloud detection is one of the important tasks for remote sensing image (RSI) preprocessing. In this paper, we utilize the thumbnail (i.e., preview image) of RSI, which contains the information of original multispectral or panchromatic imagery, to extract cloud mask efficiently. Compared with detection cloud mask from original RSI, it is more challenging to detect cloud mask using thumbnails due to the loss of resolution and spectrum information. To tackle this problem, we propose a cloud detection neural network (CDnet) with an encoder–decoder structure, a feature pyramid module (FPM), and a boundary refinement (BR) block. The FPM extracts the multiscale contextual information without the loss of resolution and coverage; the BR block refines object boundaries; and the encoder–decoder structure gradually recovers segmentation results with the same size as input image. Experimental results on the ZY-3 satellite thumbnails cloud cover validation data set and two other validation data sets (GF-1 WFV Cloud and Cloud Shadow Cover Validation Data and Landsat-8 Cloud Cover Assessment Validation Data) demonstrate that the proposed method achieves accurate detection accuracy and outperforms several state-of-the-art methods.

CDnet: CNN-Based Cloud Detection for Remote Sensing Imagery

In this paper, we propose to introduce intrinsic image decomposition priors into decomposition models for contrast enhancement. Since image decomposition is a highly ill-posed problem, we introduce constraints on both reflectance and illumination layers to yield a highly reliable solution. We regularize the reflectance layer to be piecewise constant by introducing a weighted $\ell _{1}$ norm constraint on neighboring pixels according to the color similarity, so that the decomposed reflectance would not be affected much by the illumination information. The illumination layer is regularized by a piecewise smoothness constraint. The proposed model is effectively solved by the Split Bregman algorithm. Then, by adjusting the illumination layer, we obtain the enhancement result. To avoid potential color artifacts introduced by illumination adjusting and reduce computing complexity, the proposed decomposition model is performed on the value channel in HSV space. Experiment results demonstrate that the proposed method performs well for a wide variety of images, and achieves better or comparable subjective and objective quality compared with the state-of-the-art methods.

Jingyu Yang

Papers

Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model

Landmark Image Super-Resolution by Retrieving Web Images

Cloud-Based Image Coding for Mobile Devices—Toward Thousands to One Compression

CDnet: CNN-Based Cloud Detection for Remote Sensing Imagery

Contrast Enhancement Based on Intrinsic Image Decomposition