Improving Image Quality by Camera Signal Adaptation to Lighting Conditions Mihai Negru and Sergiu Nedevschi Technical University of Cluj-Napoca, Computer Science Department Mihai.Negru@cs.utcluj.ro, Sergiu.Nedevschi@cs.utcluj.ro Abstract—The quality of images in real time vision tends to be of extreme importance. Without high quality images processing of the outside world can introduce extreme errors. Image quality is dependent on many environmental factors, such as weather and lighting conditions (sun, rain, snow, fog, mist), entering and going out of tunnels, shadows, cars headlights. This paper presents a new approach for adapting the camera response with respect to the environment’s lighting conditions. For this we model a digital camera’s response as a function of its parameters. The obtained mathematical model is vital for adapting the cameras to the environment’s lighting conditions. The most widely used camera parameters are the exposure time and amplification gain. By adjusting these parameters the image acquisition system can be less dependent on the environment’s lighting conditions and can provide better quality images for further processing. Keywords: camera model, radiometric calibration, camera adaptation, lighting conditions, stereo vision I. I NTRODUCTION In real world applications, when the processed environment is rapidly changing it is very hard to obtain high quality images. In order to improve the quality of the obtained images we must modify some of the camera parameters (amplification gain, exposure time, focus). In real time systems, the focal distance and the camera aperture are chosen in accordance to the domain of applications and are kept constant. Since the environment is not a static one, we need to develop a method of adapting the camera signal to the outside lighting conditions, i.e. we must model the response of the digital camera. This response must be a function of the camera parameters and it can be modeled by a mathematical function that relates the observed intensity (I ) of a pixel to the intensity of light (q) falling on the corresponding sensor, the current exposure time (e) and the current gain (g) settings. Thus, a camera model is a function of the type presented in equation 1. By adjusting these parameters in real time, the acquisition system becomes less dependent on the lighting conditions. I = f (q, e, g) (1) In [1] we have modeled two mathematical camera response functions: the ”linear camera model” and the ”square gain camera model”. We have also proved that the square gain camera model is more accurate than the linear one and that it can be used to estimate the camera response with minimum errors. In order to adapt the cameras to the outside lighting conditions we have developed an algorithm that tries to model the human visual system. The human eye has the ability to adapt to light intensities in a wide dynamic range, making people perceive information in almost any lighting conditions. Our algorithm is designed to use only one metric, namely image brightness or average intensity of an image. A. Related Work The output of an imaging system is a brightness (or in- tensity) image. An image acquired by a camera consists of measured intensity values which are related to scene radiance by a function called the camera response function. Knowledge of this response is necessary for computer vision algorithms which depend on scene radiance. Brightness values of pixels in an image are related to image irradiance by a non-linear function, called the radiometric response function [2]. Several investigators have described methods for recovery of the radiometric response from multiple exposures of the same scene. The radiometric response function comes from the correspondence of gray-levels between images of the same static scene taken at different exposures. In [3], Mitsunaga and Nayar approximated the function by a low degree polynomial. They were then able to obtain the coefficients of the polynomial and the exposure ratios, from rough estimates of those ratios, by alternating between recovering the response and the exposure ratios. At a sin- gle point in the image, an intensity value is related to the scene radiance by a nonlinear function. This camera response function is assumed to be the same for each point in the image. Moreover this function is monotonically increasing [4]. Nonparametric estimation of the function is performed in [5], where the estimation process is constrained by assuming the smoothness of the response function. Grossberg and Nayar [6] estimated the parameters by projecting the response function to a low dimensional space of response functions obtained from a database of real world response functions. Using this model they estimate the camera response from images of an arbitrary scene taken using different exposures. In [7], a method to estimate the radiometric response functions (of R, G and B channels) of a color camera directly from the images of an arbitrary scene taken under different illumination conditions is presented. The illumination conditions are not assumed to be known. The response function of a channel is modeled as a gamma curve and is recovered by using a constrained nonlinear minimization approach by exploiting the fact that the material properties of the scene remain constant in all the images. A unified framework for dealing with non-ideal radiometric aspects, such as spatial non-uniformity, variations due to automatic gain control (AGC), non linear response of 273 978-1-4577-1481-8/11/$26.00 ©2011 IEEE