Aesthetic-Driven Image Enhancement by Adversarial Learning

We introduce EnhanceGAN, an adversarial learning based model that performs automatic image enhancement. Traditional image enhancement frameworks typically involve training models in a fully-supervised manner, which require expensive annotations in the form of aligned image pairs. In contrast to these approaches, our proposed EnhanceGAN only requires weak supervision (binary labels on image aesthetic quality) and is able to learn enhancement operators for the task of aesthetic-based image enhancement. In particular, we show the effectiveness of a piecewise color enhancement module trained with weak supervision, and extend the proposed EnhanceGAN framework to learning a deep filtering-based aesthetic enhancer. The full differentiability of our image enhancement operators enables the training of EnhanceGAN in an end-to-end manner. We further demonstrate the capability of EnhanceGAN in learning aesthetic-based image cropping without any groundtruth cropping pairs. Our weakly-supervised EnhanceGAN reports competitive quantitative results on aesthetic-based color enhancement as well as automatic image cropping, and a user study confirms that our image enhancement results are on par with or even preferred over professional enhancement

Implementation of CQM on SSR Enhanced Images

Image enhancement is one of the key issues in high quality pictures such as digital camera and HDTV. The image enhancement targets on transforming input image as better one, so that the enhanced image solve the purpose of specific application or set of objectives. Since image clarity is very easily affected by lighting, weather, or equipment that has been used to capture the image. These conditions lead to image may suffer from loss of information. As a result, many techniques have developed known as image enhancement techniques to recover the information in an image. Contrast Stretching, Histogram equalization is a primitive and well established technique for enhancing image contrast but cannot preserve the brightness and color of the original Image and Homomorphic filtering technique has the problem of bleaching of the image. Modern technique like Single Scale Retinex (SSR) performs much better than those listed above because it is based on the color constancy theory and white balancing. Peak Signal-to-Noise Ratio (PSNR) and Calculation of Quality Measurement(CQM) are the measurable parameters which are considered in this project

Comparative Evaluation of Medical Thermal Image Enhancement Techniques for Breast Cancer Detection

Thermography is a potential medical imaging modality due to its capability in providing additional physiological information. Medical thermal images obtained from infrared thermography systems incorporate valuable temperature properties and profiles, which could indicate underlying abnormalities. The quality of thermal images is often degraded due to noise, which affects the measurement processes in medical imaging. Contrast stretching and image filtering techniques are normally adopted in medical image enhancement processes. In this study, a comparative evaluation of contrast stretching and image filtering on individual channels of true color thermal images was conducted. Their individual performances were quantitatively measured using mean square error (MSE) and peak signal to noise ratio (PSNR). The results obtained showed that contrast stretching altered the temperature profile of the original image while image filtering appeared to enhance the original image with no changes in its profile. Further measurement of both MSE and PSNR showed that the Wiener filtering method outperformed other filters with an average MSE value of 0.0045 and PSNR value of 78.739 dB. Various segmentation methods applied to both filtered and contrast stretched images proved that the filtering method is preferable for in-depth analysis

Comparative evaluation of medical thermal image enhancement techniques for breast cancer detection

Thermography is a potential medical imaging modality due to its capability in providing additional physiological information. Medical thermal images obtained from infrared thermography systems incorporate valuable temperature properties and profiles, which could indicate underlying abnormalities. The quality of thermal images is often degraded due to noise, which affects the measurement processes in medical imaging. Contrast stretching and image filtering techniques are normally adopted in medical image enhancement processes. In this study, a comparative evaluation of contrast stretching and image filtering on individual channels of true color thermal images was conducted. Their individual performances were quantitatively measured using mean square error (MSE) and peak signal to noise ratio (PSNR). The results obtained showed that contrast stretching altered the temperature profile of the original image while image filtering appeared to enhance the original image with no changes in its profile. Further measurement of both MSE and PSNR showed that the Wiener filtering method outperformed other filters with an average MSE value of 0.0045 and PSNR value of 78.739 dB. Various segmentation methods applied to both filtered and contrast stretched images proved that the filtering method is preferable for in-depth analysis

Histogram Equalization with Filtering Techniques for Enhancement of Low Quality Microscopic Blood Smear Images

This paper presents image enhancement and filtering techniques for microscope blood smear image, in order to improve low image quality that have characteristics: blurred, the diminished true color of objects which are cells , unclear boundary and low contrast between the cells and background. Therefore in this paper proposed histogram equalization (HE) technique followed with filtering techniques such as median filter. HE utilizing to adjust the contrast which based on intensity pixels values, hence able to measure image quality through image histogram as shown in results, while removing noise from the images using filtering and gamma correction parameter in order to distinguish between background and foreground (cells) to get clear borders also. These techniques have been implemented on 46 blood samples. The proposed method successfully improve the readability of the cells in the low quality of blood smear images this mean that contain more information with a good effectiveness which lead for the correct sickness detection and data analysis