Laurence Meylan

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We propose a new method to render high dynamic range images that models global and local adaptation of the human visual system. Our method is based on the center-surround Retinex model. The novelties of our method is first to use an adaptive filter, whose shape follows the image high-contrast edges, thus reducing halo artifacts common to other methods.(More)
Recent advances in the design of high dynamic range (HDR) monitors enable the display of images having a large dynamic range, close to that encountered in the real world. As their usage will increase, we will be confronted with the problem of rerendering images that have been mapped to standard dynamic range (SDR) displays so that they look natural on HDR(More)
Capturing and rendering an image that fulfills the observer’s expectations is a difficult task. This is due to the fact that the signal reaching the eye is processed by a complex mechanism before forming a percept, whereas a capturing device only retains the physical value of light intensities. It is especially difficult to render complex scenes with highly(More)
We present a tone mapping algorithm that is derived from a model of retinal processing. Our approach has two major improvements over existing methods. First, tone mapping is applied directly on the mosaic image captured by the sensor, analogous to the human visual system that applies a nonlinearity to the chromatic responses captured by the cone mosaic.(More)
We present a tone mapping algorithm that is derived from a model of retinal processing. Our approach has two major improvements over existing methods. First, tone mapping is applied directly on the mosaic image captured by the sensor, analogue to the human visual system that applies a non-linearity on the chromatic responses captured by the cone mosaic.(More)
We propose a complete digital camera workflow to capture and render high dynamic range (HDR) static scenes, from RAW sensor data to an output-referred encoded image. In traditional digital camera processing, demosaicing is one of the first operations done after scene analysis. It is followed by rendering operations, such as color correction and tone(More)
We propose a method for high dynamic range (HDR) mapping that is directly applied on the color filter array (CFA) image instead of the already demosaiced image. This rendering is closer to retinal processing where an image is acquired by a mosaic of cones and where adaptive non-linear functions apply before interpolation. Thus, in our framework, demosaicing(More)
If multiple images of a scene are available instead of a single image, we can use the additional information conveyed by the set of images to generate a higher quality image. This can be done along multiple dimensions. Super-resolution algorithms use a set of shifted and rotated low resolution images to create a high resolution image. High dynamic range(More)