Enhancement of Aerial and Medical Image using Multi resolution pyramid

Image enhancement has been an area of active research for decades. Most of the studies are aimed at improving the quality of image for better visualization. An approach for contrast enhancement utilizing multi-scale analysis is introduced. To show the effects of image enhancement, quantitative measures should be introduced. In this paper, we examine the effect of global and local enhancement using multi resolution pyramids. We identify a set of quality metric parameters for comparative performance analysis and use it to assess the enhanced output image for a number of image enhancement algorithms using pyramid

Lightness enhancement by sigmoid function

In this paper we purposed algorithm, to enhancement the contrast and lightening  of Color image .it is use to solve the problem of low lightening or non-uniform lightening. The purposed algorithm  is called (Lightening Enhancement by Sigmoid Function) " LESF", this algorithm consist of  three parts  the first  Adaptive luminance enhancement second contrast enhancement  and third Color restoration. This algorithm compared with other algorithm like  (A new nonlinear adaptive enhancement) (NNAE), MSR( multi-scale Retinex  ) and Histogram equalization  (HE).when we compared this algorithm by using entropy, time , Mean Squared Error for hue(Mea-H) and Mean Squared Error for saturation(Mea-S)    , we find The result of (LESF) have a good  result and better visual Comparing to the other methods Keywords: Image Enhancement, adaptation sigmoid function  histogram equalization , Illumination enhancement

Multi-Modal Enhancement Techniques for Visibility Improvement of Digital Images

Image enhancement techniques for visibility improvement of 8-bit color digital images based on spatial domain, wavelet transform domain, and multiple image fusion approaches are investigated in this dissertation research. In the category of spatial domain approach, two enhancement algorithms are developed to deal with problems associated with images captured from scenes with high dynamic ranges. The first technique is based on an illuminance-reflectance (I-R) model of the scene irradiance. The dynamic range compression of the input image is achieved by a nonlinear transformation of the estimated illuminance based on a windowed inverse sigmoid transfer function. A single-scale neighborhood dependent contrast enhancement process is proposed to enhance the high frequency components of the illuminance, which compensates for the contrast degradation of the mid-tone frequency components caused by dynamic range compression. The intensity image obtained by integrating the enhanced illuminance and the extracted reflectance is then converted to a RGB color image through linear color restoration utilizing the color components of the original image. The second technique, named AINDANE, is a two step approach comprised of adaptive luminance enhancement and adaptive contrast enhancement. An image dependent nonlinear transfer function is designed for dynamic range compression and a multiscale image dependent neighborhood approach is developed for contrast enhancement. Real time processing of video streams is realized with the I-R model based technique due to its high speed processing capability while AINDANE produces higher quality enhanced images due to its multi-scale contrast enhancement property. Both the algorithms exhibit balanced luminance, contrast enhancement, higher robustness, and better color consistency when compared with conventional techniques. In the transform domain approach, wavelet transform based image denoising and contrast enhancement algorithms are developed. The denoising is treated as a maximum a posteriori (MAP) estimator problem; a Bivariate probability density function model is introduced to explore the interlevel dependency among the wavelet coefficients. In addition, an approximate solution to the MAP estimation problem is proposed to avoid the use of complex iterative computations to find a numerical solution. This relatively low complexity image denoising algorithm implemented with dual-tree complex wavelet transform (DT-CWT) produces high quality denoised images

A multi-scale and morphological gradient preserving contrast

International audienceThis document outlines an algorithm which extends and enhances the regularised gradient. The regularised gradient is known to be a thin gradient. It has little noise, is multi-scale and has been extensively used, for instance, for the extraction of road markers from particularly challenging image sets. However, the intensity values taken by the regularised gradient are usually not representative of the perceived contrast across object boundaries. This limitation may prevent hierarchical segmentations based on the waterfalls or synchronous floodings from being meaningful with respect to the actual human perception of saliency. The proposed enhancement of the regularised gradient preserves the thinness and multi-scale properties of the latter whilst taking the actual contrast across different image scales into account

Adaptive multi-scale retinex algorithm for contrast enhancement of real world scenes

Contrast enhancement is a classic image restoration technique that traditionally has been performed using forms of histogram equalization. While effective these techniques often introduce unrealistic tonal rendition in real-world scenes. This paper explores the use of Retinex theory to perform contrast enhancement of real-world scenes. We propose an improvement to the Multi-Scale Retinex algorithm which enhances its ability to perform dynamic range compression while not introducing halo artifacts and greying. The algorithm is well suited to be implemented on the GPU and by doing so real-time processing speeds are achieved

A Qualitative Review of Selected Infrared Flow Visualization Processing Techniques: Contrast Enhancement and Frequency Domain Analysis

The deployment and integration of high-sensitivity infrared cameras in a transonic wind tunnel test environment has resulted in a unique capability to image aerodynamic phenomena in real-time. Multi-camera infrared flow visualization data systems are now routinely utilized at the NASA Ames Unitary Plan Wind Tunnel. The small flow-induced temperature gradients on the surface of the wind tunnel test article coupled with the high bit-depth of the infrared camera sensor makes the processing of the image data critically important. An image processing routine must enhance features of interest with minimal artifacts. Additionally, the production wind tunnel test environment demands that these processed images are made available in a real-time, automatic fashion. Therefore, any image processing routine must be computationally economical and enhance the image data with minimal input from a human operator. The following seeks to qualitatively explore selected image processing techniques by assessing their effectiveness to resolve flow features on a wind tunnel test article. A multi-scale contrast enhancement technique is introduced as well as a new implementation of a multi-scale, non-interpolated adaptive histogram equalization. Finally, a novel method is introduced that demonstrates the ability to resolve flow features imaged on bare-steel test articles possessing low emissivity. This method merges frequency domain analysis with contrast enhancement and has the potential to extend the application of infrared flow-visualization within the wind tunnel test environment

UWFormer: Underwater Image Enhancement via a Semi-Supervised Multi-Scale Transformer

Underwater images often exhibit poor quality, imbalanced coloration, and low contrast due to the complex and intricate interaction of light, water, and objects. Despite the significant contributions of previous underwater enhancement techniques, there exist several problems that demand further improvement: (i) Current deep learning methodologies depend on Convolutional Neural Networks (CNNs) that lack multi-scale enhancement and also have limited global perception fields. (ii) The scarcity of paired real-world underwater datasets poses a considerable challenge, and the utilization of synthetic image pairs risks overfitting. To address the aforementioned issues, this paper presents a Multi-scale Transformer-based Network called UWFormer for enhancing images at multiple frequencies via semi-supervised learning, in which we propose a Nonlinear Frequency-aware Attention mechanism and a Multi-Scale Fusion Feed-forward Network for low-frequency enhancement. Additionally, we introduce a specialized underwater semi-supervised training strategy, proposing a Subaqueous Perceptual Loss function to generate reliable pseudo labels. Experiments using full-reference and non-reference underwater benchmarks demonstrate that our method outperforms state-of-the-art methods in terms of both quantity and visual quality