Superpixel-based Color Transfer
- URL: http://arxiv.org/abs/1903.06010v2
- Date: Wed, 17 Sep 2025 13:56:23 GMT
- Title: Superpixel-based Color Transfer
- Authors: Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis,
- Abstract summary: We propose a fast superpixel-based color transfer method (SCT) between two images.<n>Superpixels enable to decrease the image dimension and to extract a reduced set of color candidates.
- Score: 5.746869663956391
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this work, we propose a fast superpixel-based color transfer method (SCT) between two images. Superpixels enable to decrease the image dimension and to extract a reduced set of color candidates. We propose to use a fast approximate nearest neighbor matching algorithm in which we enforce the match diversity by limiting the selection of the same superpixels. A fusion framework is designed to transfer the matched colors, and we demonstrate the improvement obtained over exact matching results. Finally, we show that SCT is visually competitive compared to state-of-the-art methods.
Related papers
- Video Frame Interpolation with Many-to-many Splatting and Spatial
Selective Refinement [83.60486465697318]
We propose a fully differentiable Many-to-Many (M2M) splatting framework to interpolate frames efficiently.
For each input frame pair, M2M has a minuscule computational overhead when interpolating an arbitrary number of in-between frames.
We extend an M2M++ framework by introducing a flexible Spatial Selective Refinement component, which allows for trading computational efficiency for quality and vice versa.
arXiv Detail & Related papers (2023-10-29T09:09:32Z) - Beyond Learned Metadata-based Raw Image Reconstruction [86.1667769209103]
Raw images have distinct advantages over sRGB images, e.g., linearity and fine-grained quantization levels.
They are not widely adopted by general users due to their substantial storage requirements.
We propose a novel framework that learns a compact representation in the latent space, serving as metadata.
arXiv Detail & Related papers (2023-06-21T06:59:07Z) - Image Reconstruction using Superpixel Clustering and Tensor Completion [21.088385725444944]
Our method divides the image into several regions that capture important textures or semantics and selects a representative pixel from each region to store.
We propose two smooth tensor completion algorithms that can effectively reconstruct different types of images from the selected pixels.
arXiv Detail & Related papers (2023-05-16T16:00:48Z) - Raw Image Reconstruction with Learned Compact Metadata [61.62454853089346]
We propose a novel framework to learn a compact representation in the latent space serving as the metadata in an end-to-end manner.
We show how the proposed raw image compression scheme can adaptively allocate more bits to image regions that are important from a global perspective.
arXiv Detail & Related papers (2023-02-25T05:29:45Z) - Saliency Enhancement using Superpixel Similarity [77.34726150561087]
Saliency Object Detection (SOD) has several applications in image analysis.
Deep-learning-based SOD methods are among the most effective, but they may miss foreground parts with similar colors.
We introduce a post-processing method, named textitSaliency Enhancement over Superpixel Similarity (SESS)
We demonstrate that SESS can consistently and considerably improve the results of three deep-learning-based SOD methods on five image datasets.
arXiv Detail & Related papers (2021-12-01T17:22:54Z) - AINet: Association Implantation for Superpixel Segmentation [82.21559299694555]
We propose a novel textbfAssociation textbfImplantation (AI) module to enable the network to explicitly capture the relations between the pixel and its surrounding grids.
Our method could not only achieve state-of-the-art performance but maintain satisfactory inference efficiency.
arXiv Detail & Related papers (2021-01-26T10:40:13Z) - Probabilistic Color Constancy [88.85103410035929]
We define a framework for estimating the illumination of a scene by weighting the contribution of different image regions.
The proposed method achieves competitive performance, compared to the state-of-the-art, on INTEL-TAU dataset.
arXiv Detail & Related papers (2020-05-06T11:03:05Z) - Multi-Scale Superpatch Matching using Dual Superpixel Descriptors [0.6875312133832078]
Over-segmentation into superpixels is a very effective dimensionality reduction strategy, enabling fast dense image processing.
The inherent irregularity of the image decomposition compared to standard hierarchical multi-resolution schemes is a problem.
We introduce the dual superpatch, a novel superpixel neighborhood descriptor.
arXiv Detail & Related papers (2020-03-09T22:04:04Z) - Texture Superpixel Clustering from Patch-based Nearest Neighbor Matching [2.84279467589473]
We propose a new Nearest Neighbor-based Superpixel Clustering (NNSC) method to generate texture-aware superpixels in a limited computational time.
arXiv Detail & Related papers (2020-03-09T21:11:21Z) - Robust superpixels using color and contour features along linear path [5.746869663956391]
We propose a framework that provides accurate and regular Superpixels with Contour Adherence using Linear Path (SCALP)<n>A contour prior is also used to prevent the crossing of image boundaries when associating a pixel to a superpixel.<n>SCALP is extensively evaluated on standard segmentation dataset, and the obtained results outperform the ones of the state-of-the-art methods.
arXiv Detail & Related papers (2019-03-17T23:00:13Z) - SCALP: Superpixels with Contour Adherence using Linear Path [5.746869663956391]
We propose a fast method to compute Superpixels with Contour Adherence using Linear Path (SCALP) in an iterative clustering framework.<n>The proposed framework produces compact and superpixels that adhere to contour contours.
arXiv Detail & Related papers (2019-03-17T19:23:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.