Most camera images are saved as 8-bit standard RGB (sRGB) compressed JPEGs. Even when JPEG compression is set to its highest quality, the encoded sRGB image has been significantly processed in terms of color and tone manipulation. This makes sRGB-JPEG images undesirable for many computer vision tasks that assume a direct relationship between pixel values and incoming light. For such applications, the RAW image format is preferred, as RAW represents a minimally processed, sensor-specific RGB image that is linear with respect to scene radiance. The drawback with RAW images, however, is that they require large amounts of storage and are not well-supported by many imaging applications. To address this issue, we present a method to encode the necessary data within an sRGB-JPEG image to reconstruct a high-quality RAW image. Our approach requires no calibration of the camera's colorimetric properties and can reconstruct the original RAW to within 0.5% error with a small memory overhead for the additional data (e.g., 128 KB). More importantly, our output is a fully self-contained 100% compliant sRGB-JPEG file that can be used as-is, not affecting any existing image workflow-the RAW image data can be extracted when needed, or ignored otherwise. We detail our approach and show its effectiveness against competing strategies.

One of the most frequently applied low-level operations in computer vision is the conversion of an RGB camera image into its luminance representation. This is also one of the most incorrectly applied operations. Even our most trusted softwares, Matlab and OpenCV, do not perform luminance conversion correctly. In this paper, we examine the main factors that make proper RGB to luminance conversion difficult, in particular: 1) incorrect white-balance, 2) incorrect gamma/tone-curve correction, and 3) incorrect equations. Our analysis shows errors up to 50% for various colors are not uncommon. As a result, we argue that for most computer vision problems there is no need to attempt luminance conversion, instead, there are better alternatives depending on the task.

Abstract Modifying the colors of an image is a fundamental editing task with a wide range of methods available. Manipulating multiple images to share similar colors is more challenging, with limited tools available. Methods such as color transfer are effective in making an image share similar colors with a target image; however, color transfer is not suitable for modifying multiple images. Approaches for color consistency for photo collections give good results when the photo collection contains similar scene content, but are not applicable for general input images. To address these gaps, we propose an application framework for achieving color consistency for multi‐image input. Our framework derives a group color theme from the input images′ individual color palettes and uses this group color theme to recolor the image collection. This group‐theme recoloring provides an effective way to ensure color consistency among multiple images and naturally lends itself to the inclusion of an additional external color theme. We detail our group‐theme recoloring approach and demonstrate its effectiveness on a number of examples.

Most camera images are saved as 8-bit standard RGB (sRGB) compressed JPEGs. Even when JPEG compression is set to its highest quality, the encoded sRGB image has been significantly processed in terms of color and tone manipulation. This makes sRGB-JPEG images undesirable for many computer vision tasks that assume a direct relationship between pixel values and incoming light. For such applications, the RAW image format is preferred, as RAW represents a minimally processed, sensor-specific RGB image with higher dynamic range that is linear with respect to scene radiance. The drawback with RAW images, however, is that they require large amounts of storage and are not well-supported by many imaging applications. To address this issue, we present a method to encode the necessary metadata within an sRGB image to reconstruct a high-quality RAW image. Our approach requires no calibration of the camera and can reconstruct the original RAW to within 0:3% error with only a 64 KB overhead for the additional data. More importantly, our output is a fully selfcontained 100% complainant sRGB-JPEG file that can be used as-is, not affecting any existing image workflow - the RAW image can be extracted when needed, or ignored otherwise. We detail our approach and show its effectiveness against competing strategies.

L0gradient minimization can be applied to an input signal to control the number of non-zero gradients. This is useful in reducing small gradients generally associated with signal noise, while preserving important signal features. In computer vision, L0gradient minimization has found applications in image denoising, 3D mesh denoising, and image enhancement. Minimizing the L0norm, however, is an NP-hard problem because of its non-convex property. As a result, existing methods rely on approximation strategies to perform the minimization. In this paper, we present a new method to perform L0gradient minimization that is fast and effective. Our method uses a descent approach based on region fusion that converges faster than other methods while providing a better approximation of the optimal L0norm. In addition, our method can be applied to both 2D images and 3D mesh topologies. The effectiveness of our approach is demonstrated on a number of examples.

Camera images saved in raw format are being adopted in computer vision tasks since raw values represent minimally processed sensor responses. Camera manufacturers, however, have yet to adopt a standard for raw images and current raw-rgb values are device specific due to different sensors spectral sensitivities. This results in significantly different raw images for the same scene captured with different cameras. This paper focuses on estimating a mapping that can convert a raw image of an arbitrary scene and illumination from one camera's raw space to another. To this end, we examine various mapping strategies including linear and non-linear transformations applied both in a global and illumination-specific manner. We show that illumination-specific mappings give the best result, however, at the expense of requiring a large number of transformations. To address this issue, we introduce an illumination-independent mapping approach that uses white-balancing to assist in reducing the number of required transformations. We show that this approach achieves state-of-the-art results on a range of consumer cameras and images of arbitrary scenes and illuminations.

Abstract This paper proposes a new approach for color transfer between two images. Our method is unique in its consideration of the scene illumination and the constraint that the mapped image must be within the color gamut of the target image. Specifically, our approach first performs a white‐balance step on both images to remove color casts caused by different illuminations in the source and target image. We then align each image to share the same ‘white axis’ and perform a gradient preserving histogram matching technique along this axis to match the tone distribution between the two images. We show that this illuminant‐aware strategy gives a better result than directly working with the original source and target image's luminance channel as done by many previous methods. Afterwards, our method performs a full gamut‐based mapping technique rather than processing each channel separately. This guarantees that the colors of our transferred image lie within the target gamut. Our experimental results show that this combined illuminant‐aware and gamut‐based strategy produces more compelling results than previous methods. We detail our approach and demonstrate its effectiveness on a number of examples.

One of the most difficult problems in Augmented Reality is how to ensure that the luminance of virtual objects embedded in a real scene is consistent with the real luminance. This paper shows an approach to identifying the illumination information from a single image of a real scene. Based on the assumption that the object used as a light probe has a globally convex shape, we can employ it to determine the illumination information in terms of the direction and the intensity.