Image contrast enhancement based on the Schrödinger operator spectrum
- URL: http://arxiv.org/abs/2406.02264v2
- Date: Tue, 29 Oct 2024 19:31:43 GMT
- Title: Image contrast enhancement based on the Schrödinger operator spectrum
- Authors: Juan M. Vargas, Taous-Meriem Laleg-Kirati,
- Abstract summary: We propose a novel image contrast enhancement method based on projecting images onto the squared eigenfunctions of the two-dimensional Schr"odinger operator.
This projection relies on a design parameter, $gamma$, which controls pixel intensity during image reconstruction.
Results demonstrate that the proposed method effectively enhances image contrast while preserving the inherent characteristics of the original image.
- Score: 0.276240219662896
- License:
- Abstract: In this study, we propose a novel image contrast enhancement method based on projecting images onto the squared eigenfunctions of the two-dimensional Schr\"odinger operator. This projection relies on a design parameter, $\gamma$, which controls pixel intensity during image reconstruction. The method's performance is evaluated using color images. The selection of $\gamma$ values is guided by priors based on fuzzy logic and clustering, preserving the spatial adjacency information of the image. Additionally, multi-objective optimization using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to determine the optimal values of $\gamma$ and the semi-classical parameter, $h$, from the 2D-SCSA. Results demonstrate that the proposed method effectively enhances image contrast while preserving the inherent characteristics of the original image, producing the desired enhancement with minimal artifacts.
Related papers
- Prompt Recovery for Image Generation Models: A Comparative Study of Discrete Optimizers [58.50071292008407]
We present the first head-to-head comparison of recent discrete optimization techniques for the problem of prompt inversion.
We find that focusing on the CLIP similarity between the inverted prompts and the ground truth image acts as a poor proxy for the similarity between ground truth image and the image generated by the inverted prompts.
arXiv Detail & Related papers (2024-08-12T21:35:59Z) - Local Binary Pattern(LBP) Optimization for Feature Extraction [0.08192907805418582]
Local binary pattern (LBP) is a powerful operator that describes the local texture features of images.
This paper provides a novel mathematical representation of the LBP by separating the operator into three matrices.
A new algorithm is proposed to optimize them for improved classification performance.
arXiv Detail & Related papers (2024-07-26T10:59:19Z) - Image-GS: Content-Adaptive Image Representation via 2D Gaussians [55.15950594752051]
We propose Image-GS, a content-adaptive image representation.
Using anisotropic 2D Gaussians as the basis, Image-GS shows high memory efficiency, supports fast random access, and offers a natural level of detail stack.
General efficiency and fidelity of Image-GS are validated against several recent neural image representations and industry-standard texture compressors.
We hope this research offers insights for developing new applications that require adaptive quality and resource control, such as machine perception, asset streaming, and content generation.
arXiv Detail & Related papers (2024-07-02T00:45:21Z) - Learn From Orientation Prior for Radiograph Super-Resolution:
Orientation Operator Transformer [8.009052363001903]
High-resolution radiographic images play a pivotal role in the early diagnosis and treatment of skeletal muscle-related diseases.
It is promising to enhance image quality by introducing single-image super-resolution (SISR) model into the radiology image field.
The conventional image pipeline, which can learn a mixed mapping between SR and denoising from the color space and inter-pixel patterns, poses a particular challenge for radiographic images with limited pattern features.
arXiv Detail & Related papers (2023-12-27T07:56:24Z) - 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) - Sub-Image Histogram Equalization using Coot Optimization Algorithm for
Segmentation and Parameter Selection [0.0]
Mean and variance based sub-image histogram equalization (MVSIHE) algorithm is one of these contrast enhancements methods proposed in the literature.
In this study, we employed one of the most recent optimization algorithms, namely, coot optimization algorithm (COA) for selecting appropriate parameters for the MVSIHE algorithm.
The results show that the proposed method can be used in the field of biomedical image processing.
arXiv Detail & Related papers (2022-05-31T06:51:45Z) - An Efficient Smoothing and Thresholding Image Segmentation Framework
with Weighted Anisotropic-Isotropic Total Variation [1.9581049654950413]
We present a multi-stage image segmentation framework that incorporates a weighted difference of anisotropic isotropic variation (AITV)
In the second stage, we threshold the smoothed image by $K$-meansizer to obtain the final result.
arXiv Detail & Related papers (2022-02-21T10:57:16Z) - Spatial-Separated Curve Rendering Network for Efficient and
High-Resolution Image Harmonization [59.19214040221055]
We propose a novel spatial-separated curve rendering network (S$2$CRNet) for efficient and high-resolution image harmonization.
The proposed method reduces more than 90% parameters compared with previous methods.
Our method can work smoothly on higher resolution images in real-time which is more than 10$times$ faster than the existing methods.
arXiv Detail & Related papers (2021-09-13T07:20:16Z) - DFM: A Performance Baseline for Deep Feature Matching [10.014010310188821]
The proposed method uses pre-trained VGG architecture as a feature extractor and does not require any additional training specific to improve matching.
Our algorithm achieves 0.57 and 0.80 overall scores in terms of Mean Matching Accuracy (MMA) for 1 pixel and 2 pixels thresholds respectively on Hpatches dataset.
arXiv Detail & Related papers (2021-06-14T22:55:06Z) - Single Image Brightening via Multi-Scale Exposure Fusion with Hybrid
Learning [48.890709236564945]
A small ISO and a small exposure time are usually used to capture an image in the back or low light conditions.
In this paper, a single image brightening algorithm is introduced to brighten such an image.
The proposed algorithm includes a unique hybrid learning framework to generate two virtual images with large exposure times.
arXiv Detail & Related papers (2020-07-04T08:23:07Z) - Residual-Sparse Fuzzy $C$-Means Clustering Incorporating Morphological
Reconstruction and Wavelet frames [146.63177174491082]
Fuzzy $C$-Means (FCM) algorithm incorporates a morphological reconstruction operation and a tight wavelet frame transform.
We present an improved FCM algorithm by imposing an $ell_0$ regularization term on the residual between the feature set and its ideal value.
Experimental results reported for synthetic, medical, and color images show that the proposed algorithm is effective and efficient, and outperforms other algorithms.
arXiv Detail & Related papers (2020-02-14T10:00:03Z)
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.