Color-accurate underwater imaging using perceptual adaptive illumination

Capturing color in water is challenging due to the heavy non-uniform attenuation of light in water across the visible spectrum, which results in dramatic hue shifts toward blue. Yet observing color in water is important for monitoring and surveillance as well as marine biology studies related to species identification, individual and group behavior, and ecosystem health and activity monitoring. Underwater robots are equipped with motor control for large scale transects but they lack sensors that enable capturing color-accurate underwater images. We present a method for color-accurate imaging in water called perceptual adaptive illumination. This method dynamically mixes the illumination of an object in a distance-dependent way using a controllable multi-color light source. The color mix compensates correctly for color loss and results in an image whose color composition is equivalent to rendering the object in air. Experiments were conducted with a color palette in the pool and at three different coral reefs sites, and with an underwater robot collecting image data with the new sensor.United States. Office of Naval Research (Project N000140911051

Perspectives in visual imaging for marine biology and ecology: from acquisition to understanding

Durden J, Schoening T, Althaus F, et al. Perspectives in Visual Imaging for Marine Biology and Ecology: From Acquisition to Understanding. In: Hughes RN, Hughes DJ, Smith IP, Dale AC, eds. Oceanography and Marine Biology: An Annual Review. 54. Boca Raton: CRC Press; 2016: 1-72

Perceptual color film restoration

The cinematographic archives represent an important part of our collective memory. In the 1950s, monopack color film became the standard on which millions of cinematographic works were recorded. A couple of decades later, it turned out that this process was chemically unstable, causing the fading of whole film stocks with time. Since the bleaching phenomenon is irreversible, photochemical restoration of faded prints is not possible, hence the incontestability of digital color restoration. Usually, a bleached color release print is the only available record of a film, and no reference color is available; thus, color and dynamic range digital restoration is dependent on historical researches and on the skill of trained technicians who are able to control the restoration parameters. This can lead to a long and frustrating restoration process. For this reason, a restoration tool is a balance between a large number of complex restoration functions used to obtain accuracy in the result and a limit on the number of these functions to maintain simplicity in their use. As an alternative solution, we start from the robust capabilities of the human vision system (HVS) to propose a tool to filter damaged frames in a quasi-unsupervised way. In fact, film color cast, caused by aging, can be considered as generic chromatic noise, and thus a spatial color synthesis algorithm can be suitable for restoring it. Moreover, a method inspired by the HVS behavior does not need any a priori information about the color cast and its magnitude. Several tests have been performed with an algorithm called ACE (Automatic Colour Equalization). ACE is just one of the phases of the restoration pipeline, and it has been modified to meet the requirements of digital film restoration practice. The basic ACE computation autonomously extracts the visual content of the frame, correcting color cast if present and expanding its dynamic range. However, this behavior is not always a good restoring solution: There are cases in which the cast has to be maintained (e.g., underwater shots) or the dynamic range must not be expanded (e.g., sunset or night shots). To this aim, new functions have been added to preserve the natural histogram shape, adding new efficacy in the restoration process. Last, to complete the set, other functions have been added to obtain satisfactory results in cases where an input frame has been excessively corrupted. Examples are presented to discuss characteristics, advantages, and limits for the use of perceptual models in digital movie color restoration

Linear techniques for image sequence processing acceleration

In this paper we present and compare two linear techniques for image sequence enhancement speed up that transform image contrast and color considering mainly the spatial relationship between the image areas. The first technique called LLL for Linear Local LUT is a local technique based on a Look Up Table transformation. The second technique (called PC2D) is a global technique based on a color mapping between some key zones of the original and corrected image. The need for speed up technique is especially important when processing high definition images and live videos. To test and compare the performance of the two proposed methods we have chosen the ACE (Automatic Color Equalization) technique, an unsupervised color equalization algorithm. We applied the techniques to the fields of digital cinema and digital film restoration (images with high definition) and underwater aquarium videos (live videos)

Image quality and automatic color equalization

In the professional movie field, image quality is mainly judged visually. In fact, experts and technicians judge and determine the quality of the film images during the calibration (post production) process. As a consequence, the quality of a restored movie is also estimated subjectively by experts [26,27]. On the other hand, objective quality metrics do not necessarily correlate well with perceived quality [28]. Moreover, some measures assume that there exists a reference in the form of an "original" to compare to, which prevents their use in digital restoration field, where often there is no reference to compare to. That is why subjective evaluation is the most used and most efficient approach up to now. But subjective assessment is expensive, time consuming and does not respond, hence, to the economic requirements of the field [29,25]. Thus, reliable automatic methods for visual quality assessment are needed in the field of digital film restoration. Ideally, a quality assessment system would perceive and measure image or video impairments just like a human being. The ACE method, for Automatic Color Equalization [1,2], is an algorithm for digital images unsupervised enhancement. Like our vision system ACE is able to adapt to widely varying lighting conditions, and to extract visual information from the environment efficaciously. We present in this paper is the use of ACE as a basis of a reference free image quality metric. ACE output is an estimate of our visual perception of a scene. The assumption, tested in other papers [3,4], is that ACE enhancing images is in the way our vision system will perceive them, increases their overall perceived quality. The basic idea proposed in this paper, is that ACE output can differ from the input more or less according to the visual quality of the input image In other word, an image appears good if it is near to the visual appearance we (estimate to) have of it. Reversely bad quality images will need "more filtering". Test and results are presented

DAF : differential ACE filtering image quality assessment by automatic color equalization

Ideally, a quality assessment system would perceive and measure image or video impairments just like a human being. But in reality, objective quality metrics do not necessarily correlate well with perceived quality [1]. Plus, some measures assume that there exists a reference in the form of an "original" to compare to, which prevents their usage in digital restoration field, where often there is no reference to compare to. That is why subjective evaluation is the most used and most efficient approach up to now. But subjective assessment is expensive, time consuming and does not respond, hence, to the economic requirements [2,3]. Thus, reliable automatic methods for visual quality assessment are needed in the field of digital film restoration. The ACE method, for Automatic Color Equalization [4,6], is an algorithm for digital images unsupervised enhancement. It is based on a new computational approach that tries to model the perceptual response of our vision system merging the Gray World and White Patch equalization mechanisms in a global and local way. Like our vision system ACE is able to adapt to widely varying lighting conditions, and to extract visual information from the environment efficaciously. Moreover ACE can be run in an unsupervised manner. Hence it is very useful as a digital film restoration tool since no a priori information is available. In this paper we deepen the investigation of using the ACE algorithm as a basis for a reference free image quality evaluation. This new metric called DAF for Differential ACE Filtering [7] is an objective quality measure that can be used in several image restoration and image quality assessment systems. In this paper, we compare on different image databases, the results obtained with DAF and with some subjective image quality assessments (Mean Opinion Score MOS as measure of perceived image quality). We study also the correlation between objective measure and MOS. In our experiments, we have used for the first image test set "Single Stimulus Continuous Quality Scale" (SSCQS) method and in the second "Double Stimulus Continuous Quality Scale" (DSCQS) method. The users, which are non-experts, were asked to identify their preferred image (between original and ACE filtered images) according to contrast, naturalness, colorfulness, quality, chromatic diversity and overall subjective preference. Test and results are presented

Tuning of perceptual technique for digital movie color restoration

In this paper we present tests and results of an automatic color fading restoration process for digitized movies. The proposed color correction method is based on the ACE model, an unsupervised color equalization algorithm based on a perceptual approach and inspired by some mechanisms of the human visual system, This perceptual approach is local, robust and does not need any user region selection or any other user supervision. However the model has a small number of parameters that has to be set once before the filtering. The tests presented in this paper aim to study these parameters and find their effect on the final result

Perceptual approach for unsupervised digital color restoration of cinematographic archives

The cinematographic archives represent an important part of our collective memory. We present in this paper some advances in automating the color fading restoration process, especially with regard to the automatic color correction technique. The proposed color correction method is based on the ACE model, an unsupervised color equalization algorithm based on a perceptual approach and inspired by some adaptation mechanisms of the human visual system, in particular lightness constancy and color constancy. There are some advantages in a perceptual approach: mainly its robustness and its local filtering properties, that lead to more effective results. The resulting technique, is not just an application of ACE on movie images, but an enhancement of ACE principles to meet the requirements in the digital film restoration field. The presented preliminary results are satisfying and promising

Underwater color constancy: enhancement of automatic live fish recognition

We present in this paper some advances in color restoration of underwater images, especially with regard to the strong and non uniform color cast which is typical of underwater images. The proposed color correction method is based on ACE model, an unsupervised color equalization algorithm. ACE is a perceptual approach inspired by some adaptation mechanisms of the human visual system, in particular lightness constancy and color constancy. A perceptual approach presents a lot of advantages: it is unsupervised. robust and has local filtering properties, that lead to more effective results. The restored images give better results when displayed or processed (fish segmentation and feature extraction). The presented preliminary results are satisfying and promising