Tone mapping

About: Tone mapping is a research topic. Over the lifetime, 1713 publications have been published within this topic receiving 48490 citations.

Edge-preserving image decomposition using L1 fidelity with L0 gradient

Abstract: We present an image decomposition method using L1 fidelity term with L0 norm of gradient to decompose an image into base layer and detail layer. Generally, the L1 fidelity should be preferable to the L2 norm when the erroneous measurements exist. It is also reported that the L0 norm of gradient is a better prior term than total variation and the L2 norm of gradient. Therefore, we combine these two benefits to obtain our base layer by adopting our method using L1 fidelity and L0 gradient. Our image decomposition method can be regarded as the fundamental tool to generate multiple image editing applications, such as image denoising, edge detection, detail enhancement, cartoon JPEG artifact removal, local tone mapping, and contrast enhancement under low backlight condition. Experimental results show that our proposed method is promising as compared to the existing methods.

Systems and methods for local tone mapping of high dynamic range images

Abstract: This is generally directed to systems and methods for local tone mapping of high dynamic range (“HDR”) images. For example, a HDR image can have its larger dynamic range mapped into the smaller dynamic range of a display device. In some embodiments, to perform the local tone mapping, a RGB to Y converter can be used to convert the input image signal to a luminance signal in the YCgCo color space, a shape adaptive filter can be used to separate the luminance signal into its illumination and reflectance components, contrast compression can be applied to the illumination component, image sharpening can be applied to the reflectance component, and the processed illumination and reflection components can be used to calculate a processed RGB signal. The dynamic range of the processed RGB signal can then be mapped into the dynamic range of the display device.

Automatic selection of optimum algorithms for high dynamic range image processing based on scene classification

Abstract: A method for processing high dynamic range (HDR) images by selecting preferred tone mapping operators and gamut mapping algorithms based on scene classification. Scenes are classified into indoor scenes, outdoor scenes, and scenes with people, and tone mapping operators and gamut mapping algorithms are selected on that basis. Prior to scene classification, the multiple images taken at various exposure values are fused into a low dynamic range (LDR) image using an exposure fusing algorithm, and scene classification is performed using the fused LDR image. Then, the HDR image generated from the multiple images are tone mapped into a LDR image using the selected tone mapping operator and then gamut mapped to the color space of the output device such as printer.

Viewing wide angle images using dynamic tone mapping

Abstract: A dynamic tone mapping technique is presented that produces a local tone map for a sub-image of a wide-angle, high dynamic range (HDR), which is used in rendering the sub-image for display. The technique generally involves first computing a global tone map of the wide-angle, HDR image in advance of rendering the sub-image. The global tone map is then used during rendering to compute a local tone map based on the average luminance and contrast of the pixels of the sub-image. In addition, the sub-image can be tone mapped as part of the rendering of a sequence of sub-images during a viewer-executed panning and/or zooming session. In this case, the local tone maps can be kept from changing too rapidly by adding a hysteresis feature to smooth out the intensity changes between successive sub-images.

An integer tone mapping operation for hdr images expressed in floating point data

Abstract: This report proposes a new tone mapping operation which is implemented in integer input and integer output. A tone mapping operation (TMO) generates a low dynamic range (LDR) image from a high dynamic range (HDR) image. Since pixel values of an HDR image are generally expressed in a floating point data format, e.g. RGBE, OpenEXR, a TMO is also implemented in floating point calculations in conventional approaches. However, it requires huge memory resources, even though a resulting LDR image is expressed in simple integer. We perform a TMO with integer input and integer output to reduce memory resources. It is experimentally confirmed with PSNR and contrast evaluations that the proposed method offers LDR images of visually high quality comparable to the conventional method.