In modern development, the world is imposing need of quality or correct information of images out of the poor contrast and distorted images. The image enhancement techniques are becoming global research phenomenon. In this paper a review of different image enhancement techniques were studied and presented. The scope and advantages of these enhancement techniques were discussed. The critical pro...

Learning a typical image enhancement pipeline involves minimization of a loss function between enhanced and reference images. While L1 and L2 losses are perhaps the most widely used functions for this purpose, they do not necessarily lead to perceptually compelling results. In this paper, we show that adding a learned no-reference image quality metric to the loss can significantly improve enhan...

Purposes of Image Contrast Enhancement Enhancements are used to improve the appearance of an image and make it easier for visual interpretation, understanding, and analysis of imagery. There are a variety of point and local image enhancement operations. Point operations modify the DN of each pixel in an image independently, such as contrast stretching; Local operations modify the value of each ...

The aim of image enhancement is to improve the interpretability or perception of information in images for human viewers, or to provide `better' input for other automated image processing techniques. Image Enhancement process is use to enhances the original image into the good image according to quality. The aim is to enhance the features of original image for the better output. In this paper, ...

Enhancing image quality has become an important issue as the volume of digital images increases exponentially and the expectation of high-quality images grows insatiably. Digitized images commonly suffer from poor visual quality due to distortions, low contrast, the deficiency of lighting, defocusing, atmospheric influences such as fog, severe compression, and transmission errors. Hence, image ...

We propose a robust image enhancement algorithm dedicated for muscle fiber specimen images captured by optical microscopes. Blur or out of focus problems are prevalent in muscle images during the image acquisition stage. Traditional image deconvolution methods do not work since they assume the blur kernels are known and also produce ring artifacts. We provide a compact framework which involves ...

This paper discusses the enhancement of images to restore detail that has been ob­ scured by blurring. An optical analog method and several digital methods of performing this processing are discussed. Sample results from one of the digital methods are shown.

With the help of Image enhancement techniques we can process an image in order to make it more appropriate for certain applications. The motive of Image enhancement is to explore the detail hidden in an image or to increase the contrast in a low contrast images. The image enhancement process try to find to increase the interpretability or perception of information in the images to provide bette...

In this entry, we provide a tutorial survey of digital image enhancement algorithms and applications. These techniques are considered throughout the image-processing literature and depend significantly on the underlying application. Thus, the survey cannot address every possible realization or application of digital image enhancement. To address this problem, we classify the methods based on tw...

Peer group image processing identifies a "peer group" for each pixel and then replaces the pixel intensity with the average over the peer group. Two parameters provide direct control over which image features are selectively enhanced: area (number of pixels in the feature) and window diameter (window size needed to enclose the feature). A discussion is given of how these parameters determine wh...