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Progressive Denoising of Monte Carlo Rendered images

COMPUTER GRAPHICS FORUM 2022年第2期41卷 1-11页

作者： Firmino, Arthur Frisvad, Jeppe Revall Jensen, Henrik Wann Luxion Aarhus Denmark Tech Univ Denmark Lyngby Denmark

image denoising based on deep learning has become a powerful tool to accelerate Monte Carlo rendering. Deep learning techniques can produce smooth images using a low sample count. Unfortunately, existing deep learning methods are biased and do not converge to the correct solution as the number of samples increase. In this paper, we propose a progressive denoising technique that aims to use denoising only when it is beneficial and to reduce its impact at high sample counts. We use Stein's unbiased risk estimate (SURE) to estimate the error in the denoised image, and we combine this with a neural network to infer a per-pixel mixing parameter. We further augment this network with confidence intervals based on classical statistics to ensure consistency and convergence of the final denoised image. Our results demonstrate that our method is consistent and that it improves existing denoising techniques. Furthermore, it can be used in combination with existing high quality denoisers to ensure consistency. In addition to being asymptotically unbiased, progressive denoising is particularly good at preserving fine details that would otherwise be lost with existing denoisers.

关键词： CCS Concepts center dot computing methodologies -> image processing Rendering Ray tracing

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Real-Time Frequency Adjustment of images and Videos

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COMPUTER GRAPHICS FORUM 2021年第2期40卷 23-37页

作者： Germano, Rafael L. Oliveira, Manuel M. Gastal, Eduardo S. L. Inst Informat UFRGS Porto Alegre RS Brazil

We present a technique for real-time adjustment of spatial frequencies in images and videos. Our method allows for both decreasing and increasing of frequencies, and is orthogonal to image resizing. Thus, it can be used to automatically adjust spatial frequencies to preserve the appearance of structured patterns during image downscaling and upscaling. By pre-computing the image's space-frequency decomposition and its unwrapped phases, these operations can be performed in real time, thanks to our novel mathematical perspective on frequency manipulation of digital images: interpreting the problem through the theory of instantaneous frequencies and phase unwrapping. To make this possible, we introduce an algorithm for the simultaneous phase unwrapping of several unordered frequency components, which also deals with the frequency-sign ambiguity of real signals. As such, our method provides theoretical and practical improvements to the concept of spectral remapping, enabling real-time performance and improved color handling. We demonstrate its effectiveness on a large number of images subject to frequency adjustment. By providing real-time control over the spatial frequencies associated with structured patterns, our technique expands the range of creative and technical possibilities for image and video processing.

关键词： CCS Concepts center dot computing methodologies -> image processing

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A Benchmark Dataset for Repetitive Pattern Recognition on Textured 3D Surfaces

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COMPUTER GRAPHICS FORUM 2021年第5期40卷 1-8页

作者： Lengauer, Stefan Sipiran, Ivan Preiner, Reinhold Schreck, Tobias Bustos, Benjamin Graz Univ Technol Inst Comp Graph & Knowledge Visualizat Graz Austria Univ Chile Dept Comp Sci Santiago Chile

In digital archaeology, a large research area is concerned with the computer-aided analysis of 3D captured ancient pottery objects. A key aspect thereby is the analysis of motifs and patterns that were painted on these objects' surfaces. In particular, the automatic identification and segmentation of repetitive patterns is an important task serving different applications such as documentation, analysis and retrieval. Such patterns typically contain distinctive geometric features and often appear in repetitive ornaments or friezes, thus exhibiting a significant amount of symmetry and structure. At the same time, they can occur at varying sizes, orientations and irregular placements, posing a particular challenge for the detection of similarities. A key prerequisite to develop and evaluate new detection approaches for such repetitive patterns is the availability of an expressive dataset of 3D models, defining ground truth sets of similar patterns occurring on their surfaces. Unfortunately, such a dataset has not been available so far for this particular problem. We present an annotated dataset of 82 different 3D models of painted ancient Peruvian vessels, exhibiting different levels of repetitiveness in their surface patterns. To serve the evaluation of detection techniques of similar patterns, our dataset was labeled by archaeologists who identified clearly definable pattern classes. Those given, we manually annotated their respective occurrences on the mesh surfaces. Along with the data, we introduce an evaluation benchmark that can rank different recognition techniques for repetitive patterns based on the mean average precision of correctly segmented 3D mesh faces. An evaluation of different incremental sampling-based detection approaches, as well as a domain specific technique, demonstrates the applicability of our benchmark. With this benchmark we especially want to address the geometry processing community, and expect it will induce novel approaches for pattern

关键词： CCS Concepts center dot Information systems -> Evaluation of retrieval results Retrieval efficiency center dot General and reference -> Metrics center dot computing methodologies -> image processing

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Progressive Refinement Imaging

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COMPUTER GRAPHICS FORUM 2020年第1期39卷 360-374页

作者： Kluge, M. Weyrich, T. Kolb, A. Univ Siegen Comp Graph Grp Siegen Germany UCL Dept Comp Sci London England

This paper presents a novel technique for progressive online integration of uncalibrated image sequences with substantial geometric and/or photometric discrepancies into a single, geometrically and photometrically consistent image. Our approach can handle large sets of images, acquired from a nearly planar or infinitely distant scene at different resolutions in object domain and under variable local or global illumination conditions. It allows for efficient user guidance as its progressive nature provides a valid and consistent reconstruction at any moment during the online refinement process. Our approach avoids global optimization techniques, as commonly used in the field of image refinement, and progressively incorporates new imagery into a dynamically extendable and memory-efficient Laplacian pyramid. Our image registration process includes a coarse homography and a local refinement stage using optical flow. Photometric consistency is achieved by retaining the photometric intensities given in a reference image, while it is being refined. Globally blurred imagery and local geometric inconsistencies due to, e.g. motion are detected and removed prior to image fusion. We demonstrate the quality and robustness of our approach using several image and video sequences, including handheld acquisition with mobile phones and zooming sequences with consumer cameras.

关键词： image processing image and video processing computational photography center dot computing methodologies -> image processing Computational photography

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