Learning Foveated Reconstruction to Preserve Perceived Image Statistics

Foveated image reconstruction recovers full image from a sparse set of samples distributed according to the human visual system's retinal sensitivity that rapidly drops with eccentricity. Recently, the use of Generative Adversarial Networks was shown to be a promising solution for such a task as they can successfully hallucinate missing image information. Like for other supervised learning approaches, also for this one, the definition of the loss function and training strategy heavily influences the output quality. In this work, we pose the question of how to efficiently guide the training of foveated reconstruction techniques such that they are fully aware of the human visual system's capabilities and limitations, and therefore, reconstruct visually important image features. Due to the nature of GAN-based solutions, we concentrate on the human's sensitivity to hallucination for different input sample densities. We present new psychophysical experiments, a dataset, and a procedure for training foveated image reconstruction. The strategy provides flexibility to the generator network by penalizing only perceptually important deviations in the output. As a result, the method aims to preserve perceived image statistics rather than natural image statistics. We evaluate our strategy and compare it to alternative solutions using a newly trained objective metric and user experiments

Dataset and metrics for predicting local visible differences

A large number of imaging and computer graphics applications require localized information on the visibility of image distortions. Existing image quality metrics are not suitable for this task as they provide a single quality value per image. Existing visibility metrics produce visual difference maps, and are specifically designed for detecting just noticeable distortions but their predictions are often inaccurate. In this work, we argue that the key reason for this problem is the lack of large image collections with a good coverage of possible distortions that occur in different applications. To address the problem, we collect an extensive dataset of reference and distorted image pairs together with user markings indicating whether distortions are visible or not. We propose a statistical model that is designed for the meaningful interpretation of such data, which is affected by visual search and imprecision of manual marking. We use our dataset for training existing metrics and we demonstrate that their performance significantly improves. We show that our dataset with the proposed statistical model can be used to train a new CNN-based metric, which outperforms the existing solutions. We demonstrate the utility of such a metric in visually lossless JPEG compression, super-resolution and watermarking.</jats:p

Depth concentrations of deuterium ions implanted into some pure metals and alloys

Pure metals (Cu, Ti, Zr, V, Pd) and diluted Pd-alloys (Pd-Ag, Pd-Pt, Pd-Ru, Pd-Rh) were implanted by 25 keV deuterium ions at fluences in the range (1.2{\div}2.3)x1022 D+/m2. The post-treatment depth distributions of deuterium ions were measured 10 days and three months after the implantation using Elastic Recoil Detection Analysis (ERDA) and Rutherford Backscattering (RBS). Comparison of the obtained results allowed to make conclusions about relative stability of deuterium and hydrogen gases in pure metals and diluted Pd alloys. Very high diffusion rates of implanted deuterium ions from V and Pd pure metals and Pd alloys were observed. Small-angle X-ray scattering revealed formation of nanosized defects in implanted corundum and titanium.Comment: 12 pages, 9 figure

Perceptual Depth Compression for Stereo Applications

Presenting stereoscopic content on 3D displays is a challenging task, usually requiring manual adjustments. A number of techniques have been developed to aid this process, but they account for binocular disparity of surfaces that are diffuse and opaque only. However, combinations of transparent as well as specular materials are common in the real and virtual worlds, and pose a significant problem. For example, excessive disparities can be created which cannot be fused by the observer. Also, multiple stereo interpretations become possible, e. g., for glass, that both reflects and refracts, which may confuse the observer and result in poor 3D experience. In this work, we propose an efficient method for analyzing and controlling disparities in computer-generated images of such scenes where surface positions and a layer decomposition are available. Instead of assuming a single per-pixel disparity value, we estimate all possibly perceived disparities at each image location. Based on this representation, we define an optimization to find the best per-pixel camera parameters, assuring that all disparities can be easily fused by a human. A preliminary perceptual study indicates, that our approach combines comfortable viewing with realistic depiction of typical specular scenes