Automatic photo adjustment is to mimic the photo retouching style of professional photographers and automatically adjust photos to the learned style. There have been many attempts to model the tone and the color adjustment globally with low-level color statistics. Also, spatially varying photo adjustment methods have been studied by exploiting high-level features and semantic label maps. Those methods are semantics-aware since the color mapping is dependent on the high-level semantic context. However, their performance is limited to the pre-computed hand-crafted features and it is hard to reflect user's preference to the adjustment. In this paper, we propose a deep neural network that models the semantics-aware photo adjustment. The proposed network exploits bilinear models that are the multiplicative interaction of the color and the contexual features. As the contextual features we propose the semantic adjustment map, which discovers the inherent photo retouching presets that are applied according to the scene context. The proposed method is trained using a robust loss with a scene parsing task. The experimental results show that the proposed method outperforms the existing method both quantitatively and qualitatively. The proposed method also provides users a way to retouch the photo by their own likings by giving customized adjustment maps.
There has been a number of studies for the automatic photo adjustment. Several methods focus on the global tonal adjustment @cite_23 , the color enhancement @cite_9 , and the personalized enhancement @cite_7 . Those methods are global adjustment approaches based on hand-crafted low-level features such as the color histogram, the scene brightness, and the highlight clipping. @cite_7 , Kapoor al proposed a method that discovers the clusters of users that have similar preferences of image enhancement for the personalized adjustment. While the concept of our method may be similar to those methods, the main difference is that we aim to discover the retouching presets that vary according to the local semantics.
@cite_9 @cite_7 @cite_23
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The aim of this paper is twofold. First, we propose a new method for enhancing the contrast of gray-value images. We use the difference of the average local contrast measures between the original and the enhanced images within a variational framework. This enables the user to intuitively control the contrast level and the scale of the enhanced details. Moreover, our model avoids large modifications of the original image histogram. Thereby it preserves the global illumination of the scene and it can cope with large areas having similar gray values. The minimizer of the proposed functional is computed by a gradient descent algorithm in connection with a polynomial approximation of the average local contrast measure. The polynomial approximation is computed via Bernstein polynomials. In the second part, the approach is extended to a variational enhancement method for color images. The model approximately preserves the hue of the original image and additionally includes a total variation term to correct the possible noise. The method requires no post- or preprocessing. The minimization problem is solved with a hybrid primal---dual algorithm. Experiments demonstrate the efficiency and the flexibility of the proposed approaches in comparison with state-of-the-art methods. This paper presents a new efficient method for recovering reliable local sets of dense correspondences between two images with some shared content. Our method is designed for pairs of images depicting similar regions acquired by different cameras and lenses, under non-rigid transformations, under different lighting, and over different backgrounds. We utilize a new coarse-to-fine scheme in which nearest-neighbor field computations using Generalized PatchMatch [ 2010] are interleaved with fitting a global non-linear parametric color model and aggregating consistent matching regions using locally adaptive constraints. Compared to previous correspondence approaches, our method combines the best of two worlds: It is dense, like optical flow and stereo reconstruction methods, and it is also robust to geometric and photometric variations, like sparse feature matching. We demonstrate the usefulness of our method using three applications for automatic example-based photograph enhancement: adjusting the tonal characteristics of a source image to match a reference, transferring a known mask to a new image, and kernel estimation for image deblurring. Color equalization algorithms exhibit a variety of behaviors described in two differing types of models: Gray World and White Patch. These two models are considered alternatives to each other in methods of color correction. They are the basis for two human visual adaptation mechanisms: Lightness Constancy and Color Constancy. The Gray World approach is typical of the Lightness Constancy adaptation because it centers the histogram dynamic, working the same way as the exposure control on a camera. Alternatively, the White Patch approach is typical of the Color Constancy adaptation, searching for the lightest patch to use as a white reference similar to how the human visual system does. The Retinex algorithm basically belongs to the White Patch family due to its reset mechanism. Searching for a way to merge these two approaches, we have developed a new chromatic correction algorithm, called Automatic Color Equalization (ACE), which is able to perform Color Constancy even if based on Gray World approach. It maintains the main Retinex idea that the color sensation derives from the comparison of the spectral lightness values across the image. We tested different performance measures on ACE, Retinex and other equalization algorithms. The results of this comparison are presented.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. In this paper, we present a color consistency technique in order to make images in the same collection share the same color style and to avoid gamut problems. Some previous methods define simple global parameter-based models and use optimizing algorithms to obtain the unknown parameters, which usually cause gamut problems in bright and dark regions. Our method is based on the range-preserving histogram specification and can enforce images to share the same color style, without resulting in gamut problems. We divide the input images into two sets having respectively high visual quality and low visual quality. The high visual quality images are used to make color balance. And then the low visual quality images are color transferred using the previous corrected high quality images. Our experiments indicate that such histogram-based color correction method is better than the compared algorithm. This paper presents a low-light image enhancement method using the variational-optimization-based Retinex algorithm. The proposed enhancement method first estimates the initial illumination and uses its gamma corrected version to constrain the illumination component. Next, the variational-based minimization is iteratively performed to separate the reflectance and illumination components. The color assignment of the estimated reflectance component is then performed to restore the color component using the input RGB color channels. Experimental results show that the proposed method can provide better enhanced result without saturation, noise amplification or color distortion. We study the problem of automatically correcting the exposure of an input image. Generic auto-exposure correction methods usually fail in individual over- under-exposed regions. Interactive corrections may fix this issue, but adjusting every photograph requires skill and time. This paper will automate the interactive correction technique by estimating the image specific S-shaped non-linear tone curve that best fits the input image. Our first contribution is a new Zone-based region-level optimal exposure evaluation, which would consider both the visibility of individual regions and relative contrast between regions. Then a detail-preserving S-curve adjustment is applied based on the optimal exposure to obtain the final output. We show that our approach enables better corrections comparing with popular image editing tools and other automatic methods. Color enhancement is a very important aspect of photo editing. Even when photographers have tens of or hundreds of photographs, they must enhance each photo one by one by manually tweaking sliders in software such as brightness and contrast, because automatic color enhancement is not always satisfactory for them. To support this repetitive manual task, we present self-reinforcing color enhancement, where the system implicitly and progressively learns the user's preferences by training on their photo editing history. The more photos the user enhances, the more effectively the system supports the user. We present a working prototype system called SelPh, and then describe the algorithms used to perform the self-reinforcement. We conduct a user study to investigate how photographers would use a self-reinforcing system to enhance a collection of photos. The results indicate that the participants were satisfied with the proposed system and strongly agreed that the self-reinforcing approach is preferable to the traditional workflow. Conventional contrast enhancement techniques often fail to produce satisfactory results for low-contrast images, and cannot be automatically applied to different images because their processing parameters must be specified manually to produce a satisfactory result for a given image. This work presents a colour-preserving contrast enhancement (CPCE) algorithm for images. Modification to images was performed in the HSV colour-space. The Hue component is preserved (unchanged), luminance modified using Contrast Limited Adaptive Histogram Equalization (CLAHE), while Saturation components were up-scaled using a derived mapping function on the approximate components of its discrete wavelet transform. Implementation was done in MATLAB and compared with CLAHE and Histogram Equalization (HE) algorithms in the RGB colour space. Subjective (visual quality inspection) and objective parameters (Peak-signal-to-noise ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Mean squared error (MSE)) were used for performance evaluation. The method produced images with the lowest MSE, AMBE, and highest PSNR when tested, yet preserved the visual quality of the image. Automatic photo enhancement is one of the long-standing goals in image processing and computational photography. While a variety of methods have been proposed for manipulating tone and colour, most automatic methods used in practice, operate on the entire image without attempting to take the content of the image into account. In this paper, we present a new framework for automatic photo enhancement that attempts to take local and global image semantics into account. Specifically, our content-aware scheme attempts to detect and enhance the appearance of human faces, blue skies with or without clouds and underexposed salient regions. A user study was conducted that demonstrates the effectiveness of the proposed approach compared to existing auto-enhancement tools. © 2012 Wiley Periodicals, Inc. Contrast enhancement algorithms have been evolved through last decades to meet the requirement of its objectives. Actually, there are two main objectives while enhancing the contrast of an image: (i) improve its appearance for visual interpretation and (ii) facilitate increase the performance of subsequent tasks (e.g., image analysis, object detection, and image segmentation). Most of the contrast enhancement techniques are based on histogram modifications, which can be performed globally or locally. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is a method which can overcome the limitations of global approaches by performing local contrast enhancement. However, this method relies on two essential hyperparameters: the number of tiles and the clip limit. An improper hyperparameter selection may heavily decrease the image quality toward its degradation. Considering the lack of methods to efficiently determine these hyperparameters, this article presents a learning-based hyperparameter selection method for the CLAHE technique. The proposed supervised method was built and evaluated using contrast distortions from well-known image quality assessment datasets. Also, we introduce a more challenging dataset containing over 6200 images with a large range of contrast and intensity variations. The results show the efficiency of the proposed approach in predicting CLAHE hyperparameters with up to 0.014 RMSE and 0.935 R2 values. Also, our method overcomes both experimented baselines by enhancing image contrast while keeping its natural aspect. We present a machine-learned ranking approach for automatically enhancing the color of a photograph. Unlike previous techniques that train on pairs of images before and after adjustment by a human user, our method takes into account the intermediate steps taken in the enhancement process, which provide detailed information on the person's color preferences. To make use of this data, we formulate the color enhancement task as a learning-to-rank problem in which ordered pairs of images are used for training, and then various color enhancements of a novel input image can be evaluated from their corresponding rank values. From the parallels between the decision tree structures we use for ranking and the decisions made by a human during the editing process, we posit that breaking a full enhancement sequence into individual steps can facilitate training. Our experiments show that this approach compares well to existing methods for automatic color enhancement. This paper presents methods for personalization of image enhancement, which could be deployed in photo editing software and also in cloud-based image sharing services. We observe that users do have different preferences for enhancing images and that there are groups of people that share similarities in preferences. Our goal is to predict enhancements for novel images belonging to a particular user based on her specific taste, to facilitate the retouching process on large image collections. To that end, we describe an enhancement framework that can learn user preferences in an individual or collaborative way. The proposed system is based on a novel interactive application that allows to collect user's enhancement preferences. We propose algorithms to predict personalized enhancements by learning a preference model from the provided information. Furthermore, the algorithm improves prediction performance as more enhancement examples are progressively added. We conducted experiments via Amazon Mechanical Turk to collect preferences from a large group of people. Results show that the proposed framework can suggest image enhancements more targeted to individual users than commercial tools with global auto-enhancement functionalities. A luminance based multi scale retinex (LB spl I.bar MSR) algorithm for the enhancement of darker images is proposed in this paper. The new technique consists only the addition of the convolution results of 3 different scales. In this way, the color noise in the shadow dark areas can be suppressed and the convolutions with different scales can be calculated simultaneously to save CPU time. Color saturation adjustment for producing more natural colors is implemented. Each spectral band can be adjusted based on the enhancement of the intensity of the band and by using a color saturation parameter. The color saturation degree can be automatically adjusted according to different types of images by compensating the original color saturation in each band. Luminance control is applied to prevent the unwanted luminance drop at the uniform luminance areas by automatically detecting the luminance drop and keeping the luminance up to certain level that is evaluated from the original image. Down-sized convolution is used for fast processing and then the result is re-sized back to the original size. Performance of the new enhancement algorithm is tested in various images captured at different lighting conditions. It is observed that the new technique outperforms the conventional MSR technique in terms of the quality of the enhanced images and computational speed. We propose a straightforward and efficient fusion-based method for enhancing weakly illumination images that uses several mature image processing techniques. First, we employ an illumination estimating algorithm based on morphological closing to decompose an observed image into a reflectance image and an illumination image. We then derive two inputs that represent luminance-improved and contrast-enhanced versions of the first decomposed illumination using the sigmoid function and adaptive histogram equalization. Designing two weights based on these inputs, we produce an adjusted illumination by fusing the derived inputs with the corresponding weights in a multi-scale fashion. Through a proper weighting and fusion strategy, we blend the advantages of different techniques to produce the adjusted illumination. The final enhanced image is obtained by compensating the adjusted illumination back to the reflectance. Through this synthesis, the enhanced image represents a trade-off among detail enhancement, local contrast improvement and preserving the natural feel of the image. In the proposed fusion-based framework, images under different weak illumination conditions such as backlighting, non-uniform illumination and nighttime can be enhanced. HighlightsA fusion-based method for enhancing various weakly illuminated images is proposed.The proposed method requires only one input to obtain the enhanced image.Different mature image processing techniques can be blended in our framework.Our method has an efficient computation time for practical applications. Adjusting photographs to obtain compelling renditions requires skill and time. Even contrast and brightness adjustments are challenging because they require taking into account the image content. Photographers are also known for having different retouching preferences. As the result of this complexity, rule-based, one-size-fits-all automatic techniques often fail. This problem can greatly benefit from supervised machine learning but the lack of training data has impeded work in this area. Our first contribution is the creation of a high-quality reference dataset. We collected 5,000 photos, manually annotated them, and hired 5 trained photographers to retouch each picture. The result is a collection of 5 sets of 5,000 example input-output pairs that enable supervised learning. We first use this dataset to predict a user's adjustment from a large training set. We then show that our dataset and features enable the accurate adjustment personalization using a carefully chosen set of training photos. Finally, we introduce difference learning: this method models and predicts difference between users. It frees the user from using predetermined photos for training. We show that difference learning enables accurate prediction using only a handful of examples. Color image enhancement is a complex and challenging task in digital imaging with abundant applications. Preserving the hue of the input image is crucial in a wide range of situations. We propose simple image enhancement algorithms, which conserve the hue and preserve the range (gamut) of the R, G, B channels in an optimal way. In our setup, the intensity input image is transformed into a target intensity image whose histogram matches a specified, well-behaved histogram. We derive a new color assignment methodology where the resulting enhanced image fits the target intensity image. We analyze the obtained algorithms in terms of chromaticity improvement and compare them with the unique and quite popular histogram-based hue and range preserving algorithm of Naik and Murthy. Numerical tests confirm our theoretical results and show that our algorithms perform much better than the Naik-Murthy algorithm. In spite of their simplicity, they compete with well-established alternative methods for images where hue-preservation is desired. Among image enhancement methods, histogram equalization (HE) has received the most attention because of its intuitive implementation quality, high efficiency, and the monotonicity of its intensity mapping function. However, HE is indiscriminate and overemphasizes the contrast around intensities with large pixel populations but little visual importance. To address this issue, we propose an HE-based method that adaptively controls the contrast gain according to the potential visual importance of intensities and pixels. Observing that in natural scenes image details are usually hidden in darker regions that have noticeable local differences, we formulate the potential visual importance on the basis of the multi-resolution, dark-pass filtered gradients in the image. Experiments show that our method is highly discriminating in terms of noises and trivial image gradients, and it guarantees great global contrast preservation. Abstract Conventional enhancements for the color display of multispectral images are based on independent contrast modifications or “stretches” of three input images. This approach is not effective if the image channels are highly correlated or if the image histograms are strongly bimodal or more complex. Any of several procedures that tend to “stretch” color saturation while leaving hue unchanged may better utilize the full range of colors for the display of image information. Two conceptually different enhancements are discussed: the “decorrelation stretch”, based on principal-component (PC) analysis, and the “stretch” of “hue”-“saturation”-intensity (HSI) transformed data. The PC transformation is scene-dependent, but the HSI transformation is invariant. Examples of images enhanced by conventional linear stretches, decorrelation stretch, and by stretches of HSI transformed data are compared. Schematic variation diagrams or two- and three-dimensional histograms are used to illustrate the “decorrelation stretch” method and the effect of the different enhancements. This paper proposes an unpaired learning method for image enhancement. Given a set of photographs with the desired characteristics, the proposed method learns a photo enhancer which transforms an input image into an enhanced image with those characteristics. The method is based on the framework of two-way generative adversarial networks (GANs) with several improvements. First, we augment the U-Net with global features and show that it is more effective. The global U-Net acts as the generator in our GAN model. Second, we improve Wasserstein GAN (WGAN) with an adaptive weighting scheme. With this scheme, training converges faster and better, and is less sensitive to parameters than WGAN-GP. Finally, we propose to use individual batch normalization layers for generators in two-way GANs. It helps generators better adapt to their own input distributions. All together, they significantly improve the stability of GAN training for our application. Both quantitative and visual results show that the proposed method is effective for enhancing images. In this paper, we describe a technique to automatically enhance the perceptual quality of an image. Unlike previous techniques, where global statistics of the image are used to determine enhancement operation, our method is local and relies on local scene descriptors and context in addition to high-level image statistics. We cast the problem of image enhancement as searching for the best transformation for each pixel in the given image and then discovering the enhanced image using a formulation based on Gaussian Random Fields. The search is done in a coarse-to-fine manner, namely by finding the best candidate images, followed by pixels. Our experiments indicate that such context-based local enhancement is better than global enhancement schemes. A user study using Mechanical Turk shows that the subjects prefer contextual and local enhancements over the ones provided by existing schemes.