Camera Response Function Estimation Research Articles

Low-light image enhancement based on virtual exposure

Under poor illumination, the image information captured by a camera is partially lost, which seriously affects the visual perception of the human. Inspired by the idea that the fusion of multiexposure images can yield one high-quality image, an adaptive enhancement framework for a single low-light image is proposed based on the strategy of virtual exposure. In this framework, the exposure control parameters are adaptively generated through a statistical analysis of the low-light image, and a virtual exposure enhancer constructed by a quadratic function is applied to generate several image frames from a single input image. Then, on the basis of generating weight maps by three factors, i.e., contrast, saturation and saliency, the image sequences and weight images are transformed by a Laplacian pyramid and Gaussian pyramid, respectively, and multiscale fusion is implemented layer by layer. Finally, the enhanced result is obtained by pyramid reconstruction rule. Compared with the experimental results of several state-of-the-art methods on five datasets, the proposed method shows its superiority on several image quality evaluation metrics. This method requires neither image calibration nor camera response function estimation and has a more flexible application range. It can weaken the possibility of overenhancement, effectively avoid the appearance of a halo in the enhancement results, and adaptively improve the visual information fidelity.

Accurate estimation of camera response function for high dynamic range measurement.

A high dynamic range surface has large reflectivity variations and measuring it with a structured light technique could cause pixel saturation that will seriously degrade the measurement accuracy. Accurate identification of saturated pixels will significantly improve the measurement accuracy. This paper introduces an accurate estimation approach to the camera response function (CRF) for high dynamic range measurement and proposes a method that uses the estimated CRF to identify the saturated pixels in captured images. We perform experiments using the proposed saturation identification method and the other existing methods to compare both the saturation identification accuracy and measurement accuracy. The experiment results verify both the accuracy and suitability of the proposed method.

Single image‐based HDR image generation with camera response function estimation

The high performance of high dynamic range (HDR) image and the limitations of sensors impel to acquire HDR images from low dynamic range (LDR) ones. The most popular methods use multiple LDR images with different exposures of the same scene as input. However, it is difficult to have multiple exposures for most of LDR images. The authors propose a single image-based method to generate HDR image based on camera response function (CRF) reconstruction. The method first estimates the CRF according to the empirical model and the input LDR image. Then, the empirical model of the inverse CRF is constructed according to the relationship between the derivatives of CRF and its inverse function; the optimal solution of inverse CRF is obtained depending on the imaging properties of the edge pixels. Finally, the HDR image is generated by performing the inverse CRF on the original LDR image. The imitation of imaging procedure inherently makes the final HDR image high quality. The experimental results indicate that the proposed approach expand image from the dark and bright regions simultaneously. The resulting metric images also illustrate that their proposed method causes lower total contrast error compared with other single image-based methods.

Improved measurement of camera response function and its performance evaluation

Estimation of camera response function (CRF) has become important in the field of computer graphics and radiance measurement to achieve accurate modeling and high dynamic range imaging. A method is proposed to provide accurate radiance for direct measurement of the CRF in this paper by using a polariscope. The experimental results indicate that the accuracy of the estimated CRF obtained by the new approach is about 5% better than that of the previous method.