Search Results (3)

The rapid advancement of 3D imaging technology and depth cameras has made depth data more accessible for applications such as virtual reality and autonomous driving. However, depth maps typically suffer from lower resolution and quality compared to color images due to sensor limitations. This paper introduces an improved approach to guided depth map super-resolution (GDSR) that effectively addresses key challenges, including the suppression of texture copying artifacts and the preservation of depth discontinuities. The proposed method integrates both local and nonlocal models within a structured framework, incorporating an adaptive bandwidth mechanism that dynamically adjusts guidance weights. Instead of relying on fixed parameters, this mechanism utilizes a distance map to evaluate patch similarity, leading to enhanced depth recovery. The local model ensures spatial smoothness by leveraging neighboring depth information, preserving fine details within small regions. On the other hand, the nonlocal model identifies similarities across distant areas, improving the handling of repetitive patterns and maintaining depth discontinuities. By combining these models, the proposed approach achieves more accurate depth upsampling with high-quality depth reconstruction. Experimental results, conducted on several datasets and evaluated using various objective metrics, demonstrate the effectiveness of the proposed method through both quantitative and qualitative assessments. The approach consistently delivers improved performance over existing techniques, particularly in preserving structural details and visual clarity. An ablation study further confirms the individual contributions of key components within the framework. These results collectively support the conclusion that the method is not only robust and accurate but also adaptable to a range of real-world scenarios, offering a practical advancement over current state-of-the-art solutions. Full article

Two of the most significant cases of extant 16th-century featherwork from Mexico are the so-called Moctezuma’s headdress and the Ahuizotl shield. While the feathers used in these artworks exhibit lightfast colors, their assembly comprises mainly organic materials, which makes them extremely fragile. Printed media, including books, catalogs, educational materials, and fine copies, offer an accessible means for audiences to document and disseminate visual aspects of delicate cultural artifacts without risking their integrity. Nevertheless, the singular brightness and iridescent colors of feathers are difficult to communicate to the viewer in printed reproductions when traditional pigments are used. This research explores the use of effect pigments (multilayered reflective structures) and improved halftoning techniques for additive printing, with the objective of enhancing the reproduction of featherwork by capturing its changing color and improving texture representation via a screen printing process. The reproduced images of featherwork exhibit significant perceptual resemblances to the originals, primarily owing to the shared presence of structural coloration. We applied structure-aware halftoning to better represent the textural qualities of feathers without compromising the performance of effect pigments in the screen printing method. Our prints show angle-dependent color, although their gamut is reduced. The novelty of this work lies in the refinement of techniques for printing full-color images by additive printing, which can enhance the 2D representation of the appearance of culturally significant artifacts. Full article

Forgery in digital images is immensely affected by the improvement of image manipulation tools. Image forgery can be classified as image splicing or copy-move on the basis of the image manipulation type. Image splicing involves creating a new tampered image by merging the components of one or more images. Moreover, image splicing disrupts the content and causes abnormality in the features of a tampered image. Most of the proposed algorithms are incapable of accurately classifying high-dimension feature vectors. Thus, the current study focuses on improving the accuracy of image splicing detection with low-dimension feature vectors. This study also proposes an approximated Machado fractional entropy (AMFE) of the discrete wavelet transform (DWT) to effectively capture splicing artifacts inside an image. AMFE is used as a new fractional texture descriptor, while DWT is applied to decompose the input image into a number of sub-images with different frequency bands. The standard image dataset CASIA v2 was used to evaluate the proposed approach. Superior detection accuracy and positive and false positive rates were achieved compared with other state-of-the-art approaches with a low-dimension of feature vectors. Full article