In this paper, we present a novel tone mapping algorithm based on multi-scale histograms and fusion (MS-Hist), for displaying wide dynamic range (WDR) images and better detection of objects such as human faces. The proposed algorithm tone maps pixels based on multiple scale local histograms, where small scales are used to preserve local contrast and large scales allow to maintain the global brightness consistency. A database of WDR images of humans depicted in high-contrast light conditions was created to validate and compare the performance of various algorithms for face detection in tasks such as biometric based identification. Our experimental results show that the proposed MS-Hist algorithm preserves image detail, brightness and high local contrast, and can benefit tasks such as face detection in WDR images.

This paper presents a hardware implementation of a mantissa/exponent-based tone mapping algorithm for wide dynamic range (WDR) images. The algorithm performs tone mapping by using a global compression model for the pixel intensities combined with a local contrast enhancement model. The pixel intensities of the WDR images used in this paper are represented in a mantissa/exponent format produced by an innovative WDR imager which takes advantage of a multi-reset technique during the capture process. The algorithm has been implemented on FPGA and designed to be very small, fast, power-efficient and has the potential to be directly integrated into the same chip as the imager. Experimental results performed by using different images show that our implementation is reliable and efficient.

In this paper, we present a modified tone mapping algorithm for displaying wide dynamic range images with good brightness. Our algorithm embeds an automatic computation of the key parameter controlling the brightness of tone mapped images, which make use of statistical analysis of WDR images. This enables to prevent unnecessary or cumbersome manual tuning of parameters as often found in tone mapping algorithms. Experimental results performed on different WDR images show that we are able to get images which are visually pleasant and which have good brightness and contrast. The good performance of our algorithm is also confirmed numerically through the use of the tone mapping quality index (TMQI), an objective image quality measure.

In this paper, we present a modified hybrid global and local tone mapping algorithm for displaying wide dynamic range (WDR) images with reduced halo artifacts. Since halo artifacts are commonly found in local tone mapping algorithms, which may produce false edges or false patterns when performing edge detection or pattern recognition algorithms on the generated tone mapped images, we propose a new Gaussian-based filter for reducing these halo artifacts. Experimental results performed on different WDR images show that we are able to get images which are visually pleasant.

In this paper, a diffusion-based generic demosaicing algorithm is proposed which can be used for various sensor images captured by digital cameras equipped with various RGB color filter arrays. This algorithm improves our previous edge-sensing generic demosaicing algorithm by enhancing the computation of the green band. In fact, since the green band plays a major and crucial role in the performance of the edge-sensing generic demosaicing algorithm, a diffusion-based model is used for reducing the errors generated when computing the green band. A series of tests has been made on images of the Kodak database, and our diffusion-based demosaicing algorithm performs better than the edge-sensing generic demosaicing algorithm in regard to both subjective and objective evaluation.

In this study, the authors analyse two well‐known image quality metrics, peak‐signal‐to‐noise ratio (PSNR) as well as structural similarity index measure (SSIM), and the authors derive an analytical relationship between them which works for some kinds of common image degradations such as Gaussian blur, additive Gaussian noise, Jpeg and Jpeg2000 compressions. The analytical relationship brings more clarity on the interpretation of PSNR and SSIM values, explains some differences found between these quality measures in the literature and confirms some experimental observations regarding these measures. A series of tests realised on images from the Kodak database give a better understanding of the performance of SSIM and PSNR in assessing image quality.

In this paper, we introduce a new algorithm that can be used for reducing aliasing in images. Our algorithm, which is derived from the standard diffusion equation, cancels the aliasing of step edges found in images by reducing their curvature while preserving their contrast through a high-pass filter. Our algorithm can be seen as an adaptive level-curve method in which diffusion is carried out in the normal direction of the gradient. Experimental tests based on different grey-level images show that our algorithm efficiently reduces aliasing, which is confirmed through objective and subjective quality evaluations.

In this paper, we introduce a new demosaicing algorithm that can be used for various sensor images captured by digital cameras equipped with various red-green-blue color filter arrays. Our algorithm enhances the universal demosaicing algorithm of Lukac et al by defining a new spectral interpolation model that exploits not only the information on the color of pixels but also the relative distance between neighboring pixels within an image. Moreover, we include an edge-detection model that makes our algorithm adaptive and reduces the presence of color shifts and artifacts. A series of tests has been made on images of the Kodak database, and our algorithm performs better than the universal demosaicing algorithm with regard to both subjective and objective evaluation. The versatility of our demosaicing algorithm is also highlighted through an application to the issue of color image resampling, and we obtain conclusive experimental results.

In this paper, we analyse two well-known objective image quality metrics, the peak-signal-to-noise ratio (PSNR) as well as the structural similarity index measure (SSIM), and we derive a simple mathematical relationship between them which works for various kinds of image degradations such as Gaussian blur, additive Gaussian white noise, jpeg and jpeg2000 compression. A series of tests realized on images extracted from the Kodak database gives a better understanding of the similarity and difference between the SSIM and the PSNR.