Colour image smoothing through a soft-switching mechanism using a graph model

[EN] In this study, the authors propose a soft-switching ¿lter to improve the performance of recent colour image smoothing ¿lters when processing homogeneous image regions. The authors use a recent ¿lter mixed with the classical arithmetic mean ¿lter (AMF). The recent method is used to process image pixels close to edges, texture and details and the AMF is only used to process homogeneous regions. To this end, the authors propose a method based on the graph theory to distinguish image details and homogeneous regions and to perform a soft switching between the two ¿lters. Experimental results show that the proposed method provides improved results which supports the appropriateness of the graph theory-based method and suggests that the same structure can be used to improve the performance of other non-linear colour image smoothing methods.The authors acknowledge the support of Spanish Ministry of Science and Innovation under grant MTM2009-12872-C02-01, Spanish Ministry of Science and Technology under grant MTM2010-18539 and DGCYT under grant MTM2009-08933Jordan Lluch, C.; Morillas, S.; Sanabria Codesal, E. (2012). Colour image smoothing through a soft-switching mechanism using a graph model. IET Image Processing. 6(9):1293-1298. doi:10.1049/IET-IPR.2011.0164S129312986

Partition based vector filtering technique for suppression of noise in digital color images

A partition-based adaptive vector filter is proposed for the restoration of corrupted digital color images. The novelty of the filter lies in its unique three-stage adaptive estimation. The local image structure is first estimated by a series of center-weighted reference filters. Then the distances between the observed central pixel and estimated references are utilized to classify the local inputs into one of preset structure partition cells. Finally, a weighted filtering operation, indexed by the partition cell, is applied to the estimated references in order to restore the central pixel value. The weighted filtering operation is optimized off-line for each partition cell to achieve the best tradeoff between noise suppression and structure preservation. Recursive filtering operation and recursive weight training are also investigated to further boost the restoration performance. The proposed filter has demonstrated satisfactory results in suppressing many distinct types of noise in natural color images. Noticeable performance gains are demonstrated over other prior-art methods in terms of standard objective measurements, the visual image quality and the computational complexity

Color and luminance correction and calibration system for LED video screens

Recent years have seen a surge in the popularity of Light emitting diode (LED) video screens, which have come to be a critical part of how the world of show business and corporate events are seen by their audiences. LED video screens are bright, visually attractive, can stand severe weather conditions, and consume far less power than CRT technology. In LED screens technology, pixels are composed of three primary LED colors: red, green, and blue (RGB). Using the primary colored LEDs provide the ability to generate variety of color hues, saturations and values. However, the RGB LEDs in the screen's pixels have different luminance and color due to the LEDs themselves. These differences seriously destroy the white balance of the LED pixels and modules, and make the picture color aberration, blotchy and patchy. To overcome these problems, different techniques and methodologies has been proposed in the literature. The main drawbacks of these techniques are the cost-effectiveness in the sense they provide mediocre resolution. In this thesis, a new and cost-effective methodology and technique is proposed to correct the color and the luminance of LED video screens while maintaining a high quality and high resolution image display. Also, a new developed algorithm is proposed to fit different color and brightness calibration purposes. The proposed algorithm is based on the CIE Commission Internationale de l'Eclairage standards. The technique and methodology have been implemented, in collaboration with LSI SACO Technologies Inc., using fully automated robotic spectrometer system and achieved the targeted goals

Colour image denoising by eigenvector analysis of neighbourhood colour samples

[EN] Colour image smoothing is a challenging task because it is necessary to appropriately distinguish between noise and original structures, and to smooth noise conveniently. In addition, this processing must take into account the correlation among the image colour channels. In this paper, we introduce a novel colour image denoising method where each image pixel is processed according to an eigenvector analysis of a data matrix built from the pixel neighbourhood colour values. The aim of this eigenvector analysis is threefold: (i) to manage the local correlation among the colour image channels, (ii) to distinguish between flat and edge/textured regions and (iii) to determine the amount of needed smoothing. Comparisons with classical and recent methods show that the proposed approach is competitive and able to provide significative improvements.Latorre-Carmona, P.; Miñana, J.; Morillas, S. (2020). Colour image denoising by eigenvector analysis of neighbourhood colour samples. 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Mathematical Approaches for Image Enhancement Problems

This thesis develops novel techniques that can solve some image enhancement problems using theoretically and technically proven and very useful mathematical tools to image processing such as wavelet transforms, partial differential equations, and variational models. Three subtopics are mainly covered. First, color image denoising framework is introduced to achieve high quality denoising results by considering correlations between color components while existing denoising approaches can be plugged in flexibly. Second, a new and efficient framework for image contrast and color enhancement in the compressed wavelet domain is proposed. The proposed approach is capable of enhancing both global and local contrast and brightness as well as preserving color consistency. The framework does not require inverse transform for image enhancement since linear scale factors are directly applied to both scaling and wavelet coefficients in the compressed domain, which results in high computational efficiency. Also contaminated noise in the image can be efficiently reduced by introducing wavelet shrinkage terms adaptively in different scales. The proposed method is able to enhance a wavelet-coded image computationally efficiently with high image quality and less noise or other artifact. The experimental results show that the proposed method produces encouraging results both visually and numerically compared to some existing approaches. Finally, image inpainting problem is discussed. Literature review, psychological analysis, and challenges on image inpainting problem and related topics are described. An inpainting algorithm using energy minimization and texture mapping is proposed. Mumford-Shah energy minimization model detects and preserves edges in the inpainting domain by detecting both the main structure and the detailed edges. This approach utilizes faster hierarchical level set method and guarantees convergence independent of initial conditions. The estimated segmentation results in the inpainting domain are stored in segmentation map, which is referred by a texture mapping algorithm for filling textured regions. We also propose an inpainting algorithm using wavelet transform that can expect better global structure estimation of the unknown region in addition to shape and texture properties since wavelet transforms have been used for various image analysis problems due to its nice multi-resolution properties and decoupling characteristics