This paper presents a method for recovering high dynamic range (HDR) radiance maps from photographs taken with conventional imaging equipment. The method uses multiple photographs of the same scene taken with different exposure settings to recover the response function of the imaging process, assuming reciprocity. Once the response function is known, the algorithm can fuse the multiple photographs into a single HDR radiance map, where pixel values are proportional to true radiance values in the scene. The method is applicable in various areas of computer graphics, including image-based modeling, image compositing, and image processing. Applications include synthesizing realistic motion blur and simulating the human visual system's response. The paper also discusses the challenges of nonlinear image response and how the method addresses them. The algorithm is based on the physical property of reciprocity in imaging systems and uses a least-squares approach to recover the response function. The method is demonstrated on images acquired with both photochemical and digital imaging processes. The paper also includes a MATLAB implementation of the algorithm for solving the linear system that minimizes the objective function. The results show that the method can accurately recover HDR radiance maps, which can be used for various applications in computer graphics.This paper presents a method for recovering high dynamic range (HDR) radiance maps from photographs taken with conventional imaging equipment. The method uses multiple photographs of the same scene taken with different exposure settings to recover the response function of the imaging process, assuming reciprocity. Once the response function is known, the algorithm can fuse the multiple photographs into a single HDR radiance map, where pixel values are proportional to true radiance values in the scene. The method is applicable in various areas of computer graphics, including image-based modeling, image compositing, and image processing. Applications include synthesizing realistic motion blur and simulating the human visual system's response. The paper also discusses the challenges of nonlinear image response and how the method addresses them. The algorithm is based on the physical property of reciprocity in imaging systems and uses a least-squares approach to recover the response function. The method is demonstrated on images acquired with both photochemical and digital imaging processes. The paper also includes a MATLAB implementation of the algorithm for solving the linear system that minimizes the objective function. The results show that the method can accurately recover HDR radiance maps, which can be used for various applications in computer graphics.