Optimisation of Convolution-Based Image Lightness Processing.

In the convolutional retinex approach to image lightness processing, an image is filtered by a centre/surround operator that is designed to mitigate the effects of shading (illumination gradients), which in turn compresses the dynamic range. Typically, the parameters that define the shape and extent of the filter are tuned to provide visually pleasing results, and a mapping function such as a logarithm is included for further image enhancement. In contrast, a statistical approach to convolutional retinex has recently been introduced, which is based upon known or estimated autocorrelation statistics of the image albedo and shading components.

Improving the perception of low-light enhanced images.

Improving images captured under low-light conditions has become an important topic in computational color imaging, as it has a wide range of applications. Most current methods are either based on handcrafted features or on end-to-end training of deep neural networks that mostly focus on minimizing some distortion metric -such as PSNR or SSIM- on a set of training images. However, the minimization of distortion metrics does not mean that the results are optimal in terms of perception (i.

Performance Comparison of Classical Methods and Neural Networks for Colour Correction.

Colour correction is the process of converting RAW RGB pixel values of digital cameras to a standard colour space such as CIE XYZ. A range of regression methods including linear, polynomial and root-polynomial least-squares have been deployed. However, in recent years, various neural network (NN) models have also started to appear in the literature as an alternative to classical methods.

A Rehabilitation of Pixel-Based Spectral Reconstruction from RGB Images.

Recently, many deep neural networks (DNN) have been proposed to solve the spectral reconstruction (SR) problem: recovering spectra from RGB measurements. Most DNNs seek to learn the relationship between an RGB viewed in a given spatial context and its corresponding spectra. Significantly, it is argued that the same RGB can map to different spectra depending on the context with respect to which it is seen and, more generally, that accounting for spatial context leads to improved SR.

Weibull Tone Mapping (WTM) for the Enhancement of Underwater Imagery.

Domain experts prefer interactive and targeted control-point tone mapping operations (TMOs) to enhance underwater image quality and feature visibility; though this comes at the expense of time and training. In this paper, we provide end-users with a simpler and faster interactive tone-mapping approach. This is built upon Weibull Tone Mapping (WTM) theory; introduced in previous work as a preferred tool to describe and improve domain expert TMOs.

Matched illumination: using light modulation as a proxy for a color filter that makes a camera more colorimetric.

In previous work, it was shown that a camera can theoretically be made more colorimetric-its RGBs become more linearly related to XYZ tristimuli-by placing a specially designed color filter in the optical path. While the prior art demonstrated the principle, the optimal color-correction filters were not actually manufactured. In this paper, we provide a novel way of creating the color filtering effect without making a physical filter: we modulate the spectrum of the light source by using a spectrally tunable lighting system to recast the prefiltering effect from a lighting perspective.

On the Optimization of Regression-Based Spectral Reconstruction.

Spectral reconstruction (SR) algorithms attempt to recover hyperspectral information from RGB camera responses. Recently, the most common metric for evaluating the performance of SR algorithms is the Mean Relative Absolute Error (MRAE)-an ℓ1 relative error (also known as percentage error). Unsurprisingly, the leading algorithms based on Deep Neural Networks (DNN) are trained and tested using the MRAE metric.

A Mathematical Investigation into the Design of Prefilters That Make Cameras More Colorimetric.

By placing a color filter in front of a camera we make new spectral sensitivities. The Luther-condition optimization solves for a color filter so that the camera's filtered sensitivities are as close to being linearly related to the XYZ color matching functions (CMFs) as possible, that is, a filter is found that makes the camera more colorimetric. Arguably, the more general Vora-Value approach solves for the filter that best matches all possible target spectral sensitivity sets (e.

Designing Color Filters That Make Cameras More Colorimetric.

When we place a colored filter in front of a camera the effective camera response functions are equal to the given camera spectral sensitivities multiplied by the filter spectral transmittance. In this article, we solve for the filter which returns the modified sensitivities as close to being a linear transformation from the color matching functions of the human visual system as possible. When this linearity condition - sometimes called the Luther condition- is approximately met, the 'camera+filter' system can be used for accurate color measurement.

Physically Plausible Spectral Reconstruction.

Spectral reconstruction algorithms recover spectra from RGB sensor responses. Recent methods-with the very best algorithms using deep learning-can already solve this problem with good spectral accuracy. However, the recovered spectra are physically incorrect in that they do not induce the RGBs from which they are recovered.

Physical-based optimization for non-physical image dehazing methods.

Images captured under hazy conditions (e.g. fog, air pollution) usually present faded colors and loss of contrast.

Providing a Single Ground-Truth for Illuminant Estimation for the ColorChecker Dataset.

The ColorChecker dataset is one of the most widely used image sets for evaluating and ranking illuminant estimation algorithms. However, this single set of images has at least 3 different sets of ground-truth (i.e.

Colour and illumination in computer vision.

In computer vision, illumination is considered to be a problem that needs to be 'solved'. The colour cast due to illumination is removed to support colour-based image recognition and stable tracking (in and out of shadows), among other tasks. In this paper, I review historical and current algorithms for illumination estimation.

Estimating individual cone fundamentals from their color-matching functions.

Estimation of individual spectral cone fundamentals from color-matching functions is a classical and longstanding problem in color science. In this paper we propose a novel method to carry out this estimation based on a linear optimization technique, employing an assumption of a priori knowledge of the retinal absorptance functions. The result is an estimation of the combined lenticular and macular filtration for an individual, along with the nine coefficients in the linear combination that relates their color-matching functions to their estimated spectral-cone fundamentals.

The Reproduction Angular Error for Evaluating the Performance of Illuminant Estimation Algorithms.

The angle between the RGBs of the measured illuminant and estimated illuminant colors-the recovery angular error-has been used to evaluate the performance of the illuminant estimation algorithms. However we noticed that this metric is not in line with how the illuminant estimates are used. Normally, the illuminant estimates are `divided out' from the image to, hopefully, provide image colors that are not confounded by the color of the light.

Spectral edge: gradient-preserving spectral mapping for image fusion.

This paper describes a novel approach to image fusion for color display. Our goal is to generate an output image whose gradient matches that of the input as closely as possible. We achieve this using a constrained contrast mapping paradigm in the gradient domain, where the structure tensor of a high-dimensional gradient representation is mapped exactly to that of a low-dimensional gradient field which is then reintegrated to form an output.

Color correction using root-polynomial regression.

Cameras record three color responses (RGB) which are device dependent. Camera coordinates are mapped to a standard color space, such as XYZ-useful for color measurement-by a mapping function, e.g.

Chromatic illumination discrimination ability reveals that human colour constancy is optimised for blue daylight illuminations.

The phenomenon of colour constancy in human visual perception keeps surface colours constant, despite changes in their reflected light due to changing illumination. Although colour constancy has evolved under a constrained subset of illuminations, it is unknown whether its underlying mechanisms, thought to involve multiple components from retina to cortex, are optimised for particular environmental variations. Here we demonstrate a new method for investigating colour constancy using illumination matching in real scenes which, unlike previous methods using surface matching and simulated scenes, allows testing of multiple, real illuminations.

Reducing integrability error of color tensor gradients for image fusion.

To overcome the difficulties in applying gradient-based operators to color images, Di Zenzo introduced the color tensor, an operator that provides a gradient field for multichannel images. An elegant application for this operator was developed in the domain of multichannel image visualization: Socolinsky and Wolff proposed to reintegrate Di Zenzo's gradient by solving a Poisson equation, yielding a greyscale representation of the multispectral contrast of the input image. Di Zenzo's gradients are, however, generally not integrable and some approximation must be introduced.

Spectral sharpening by spherical sampling.

There are many works in color that assume illumination change can be modeled by multiplying sensor responses by individual scaling factors. The early research in this area is sometimes grouped under the heading "von Kries adaptation": the scaling factors are applied to the cone responses. In more recent studies, both in psychophysics and in computational analysis, it has been proposed that scaling factors should be applied to linear combinations of the cones that have narrower support: they should be applied to the so-called "sharp sensors.

A new spectrally sharpened sensor basis to predict color naming, unique hues, and hue cancellation.

When light is reflected off a surface, there is a linear relation between the three human photoreceptor responses to the incoming light and the three photoreceptor responses to the reflected light. Different colored surfaces have different linear relations. Recently, Philipona and O'Regan (2006) showed that when this relation is singular in a mathematical sense, then the surface is perceived as having a highly nameable color.

Constrained pseudo-Brownian motion and its application to image enhancement.

Brownian motion is a random process that finds application in many fields, and its relation to certain color perception phenomena has recently been observed. On this ground, Marini and Rizzi developed a retinex algorithm based on Brownian motion paths. However, while their approach has several advantages and delivers interesting results, it has a high computational complexity.

Lookup-table-based gradient field reconstruction.

In computer vision, there are many applications, where it is advantageous to process an image in the gradient domain and then reintegrate the gradient field: important examples include shadow removal, lightness calculation, and data fusion. A serious problem with this approach is that the reconstruction step often introduces artefacts-commonly, smoothed and smeared edges-to the recovered image. This is a result of the inherent ill-posedness of reintegrating a nonintegrable field.

Analytic solution for separating spectra into illumination and surface reflectance components.

The measured light spectrum is the result of an illuminant interacting with a surface. The illuminant spectral power distribution multiplies the surface spectral reflectance function to form a color signal--the light spectrum that gives rise to our perception. Disambiguation of the two factors, illuminant and surface, is difficult without prior knowledge.

Metamer-set-based approach to estimating surface reflectance from camera RGB.

We present an approach to estimating the reflectance of a surface given its camera response. In this approach we first solve the general form of this problem: we calculate the set of all possible surface reflectances, called the metamer set, and then choose a member from this set. Three possibilities in choosing a single reflectance are described here.